Application of abnormal event detection technology to hydrocracking units

ABSTRACT

The present invention is a method for detecting an abnormal event for process units of a hydrocracking unit. The method compares the operation of the process units to a model developed by principle components analysis of normal operation for these units. If the difference between the operation of a process unit and the normal operation indicates an abnormal condition, then the cause of the abnormal condition is determined and corrected.

This application claims the benefit of U.S. Provisional application60/609,161 filed Sep. 10, 2004.

BACKGROUND OF THE INVENTION

The Hydro Desulfurization and Cracking unit (HDC) is an importantprocess unit within a petroleum refinery. The HDC converts heavyaromatic compounds, typically a combination of cycle oil and cokernaptha feeds, into lighter products which can be blended into gasolineand jet fuels. The primary processing equipment for an HDC are multiplesequential fixed bed reactors (for hydrocracking and hydrotreating) andproduct fractionation columns. Due to the fast dynamics of the process,the highly exothermic kinetics of the reactions, and the large degree ofinteraction between the process equipment of the HDC, abnormal processoperations can arise which cause the HDC to deviate from the normaloperating state. Abnormal operations of the HDC can have significantsafety and economic consequences. These situations can cause catalyst orequipment damage, lost production, environmental emissions, injuries orfatalities. A primary responsibility of the console operator is toidentify the root cause of an abnormal situation and to performcorrective actions within sufficient time to avoid potentially severeconsequences.

The current industry practice is to use a combination of base andadvanced process control applications to automatically mitigate minorprocess disturbances. The current industry practice also relies on humanintervention for moderate abnormal operations and automated emergencyshutdown systems for severe abnormal operations. At present, the consoleoperator is notified of the onset of an abnormal condition throughprocess alarms. These alarms are triggered when key process measurements(temperatures, pressures, flows, levels and compositions) violate staticoperating ranges. This notification technology is challenged to providetimely alarms while sustaining an acceptable rate of false notificationswhen the key measurements are correlated for complicated processes suchas an HDC.

For the typical HDC unit, there are in excess of 550 critical processmeasurements. Under the conventional Distributed Control System (“DCS”)system, the operator must survey the critical sensors presented in bothtabular and trend format, validate the behavior against expected normaloperating values, and discover potential problem(s).

Due to the large number of sensors in an HDC, the onset of abnormalitycan easily be overlooked. With the current DCS based monitoringtechnology, the only automated detection assistance an operator has isthe DCS alarm system which is based on the alarming of individualsensors upon violation of predetermined limits. Due to the complexityand the fast dynamics of an HDC, this type of notification is oftendelivered too late to enable the console operator to have sufficienttime to identify and take preventive action to mitigate the problem. Thepresent invention provides a more effective notification to the operatorof the HDC.

SUMMARY OF THE INVENTION

The invention is a method for detecting an abnormal event for severalprocess units of an HDC. The method compares the operations of severalof the process units to a model of normal operation for those units. Ifthe difference between the sensor values and the model for normaloperation exceed defined tolerances, the system alerts the operator of aprobable abnormal condition in a process unit. The system also providesthe operator with a hierarchical display of the sensor values which mostdeviated from the model for normal operation. The console operatorutilizes this information to diagnose the underlying cause of theabnormal operation and take corrective action. Multivariate statisticalmodels and engineering models, such as material and energy balances, areused to identify abnormal operations.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows how the information in the online system flows through thevarious transformations, model calculations, fuzzy Petri nets andconsolidation to arrive at a summary trend which indicates thenormality/abnormality of the process areas.

FIG. 2 shows a valve flow plot to the operator as a simple x-y plot.

FIG. 3 shows three dimensional redundancy expressed as a PCA model.

FIG. 4 shows a schematic diagram of a fuzzy network setup.

FIG. 5 shows a schematic diagram of the overall process for developingan abnormal event application.

FIG. 6 shows a schematic diagram of the anatomy of a process controlcascade.

FIG. 7 shows a schematic diagram of the anatomy of a multivariableconstraint controller, MVCC.

FIG. 8 shows a schematic diagram of the on-line inferential estimate ofcurrent quality.

FIG. 9 shows the KPI analysis of historical data.

FIG. 10 shows a diagram of signal to noise ratio.

FIG. 11 shows how the process dynamics can disrupt the correlationbetween the current values of two measurements.

FIG. 12 shows the probability distribution of process data.

FIG. 13 shows illustration of the press statistic.

FIG. 14 shows the two dimensional energy balance model.

FIG. 15 shows a typical stretch of Flow, Valve Position, and DeltaPressure data with the long period of constant operation.

FIG. 16 shows a type 4 fuzzy discriminator.

FIG. 17 shows a flow versus valve paraeto chart.

FIG. 18 shows a schematic diagram of operator suppression logic.

FIG. 19 shows a schematic diagram of event suppression logic.

FIG. 20 shows the setting of the duration of event suppression.

FIG. 21 shows the event suppression and the operator suppressiondisabling predefined sets of inputs in the PCA model.

FIG. 22 shows how design objectives are expressed in the primaryinterfaces used by the operator.

FIG. 23 shows the operator overview of the HDC operation decomposed into15 individual monitors.

FIG. 24 shows that the R3 Unusual Tags and R3 Extreme Op have a warningalert.

FIG. 25 shows that clicking on the red triangle on the R3 Unusual Tagsdisplay brings up this pareto chart indicating that the residual ofsensor AI092 is outside of its tolerance limit.

FIG. 26 shows the trends of the process measurement and the modelpredictions of the sensors for the Pareto chart of FIG. 25.

FIG. 27 shows a Pareto ranking of the valve-flow models sorted by thenormalized deviation error.

FIG. 28 shows the details of the valve-flow model obtained from the barchart of FIG. 27.

FIG. 29 shows the Fuzzy Logic network for the Stg1 LP Separator Levelengineering model.

FIG. 30 shows a schematic diagram of a hydrocracker unit.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The present invention is a method to provide abnormal event detection(AED) to the operator indicating that sections of a petroleum refineryhydrocracker unit are not functioning properly.

The method uses fuzzy logic (described below) to inspect multiplesupportive evidence of abnormal situations that contribute to anoperational problem and estimates its probability in realtime. Theprobability is presented in a continuous format to alert the operator.This method includes a set of tools which enable the operator to derivethe root cause of a problem for focused action. This approach has beendemonstrated to provide the operator with an advanced warning of theonset of abnormal operation that can be minutes to hours sooner than thealarm system. With additional time, the operator is able to take actionsooner preventing escalation of the event. This method has beensuccessfully applied to the HDC.

The HDC application uses specific operational knowledge of the processin combination with indications from Principal Component Analysismodels, engineering models, and relevant sensor readings. A fuzzy logicnetwork aggregates the evidence and indicates the confidence level of apotential problem. Therefore, the network can detect a problem withhigher confidence at its early stage and provide valuable time for theoperator to make compensatory or corrective actions to avoid anoperational incident on the HDC. For a more detailed description offuzzy networks, see Appendix 1.

The HDC unit is divided into equipment groups (referred to as keyfunctional sections, or operational sections). These equipment groupsmay be different for different HDC units depending on its design. Theprocedure for choosing equipment groups which include specific processunits of the hydrocracking unit is described in Appendix 1.

FIG. 30 shows a schematic diagram of a typical HDC unit. In thepreferred embodiment for this HDC unit, the present invention dividesthe HDC operation into key functional sections. A typical HDC unit canbe divided as follows:

-   -   1. 1st & 2nd Stage Reactors (R1 & R2)    -   2. 3rd Stage Reactor (R3)    -   3. Fractionation Section (stabilizer and splitter)

Besides monitoring these functional areas, this invention also checksfor consistency between the following:

-   -   1. Flow measurements and valve position for key control valves    -   2. Redundant level sensors in the high & low pressure hydrogen        recovery units    -   3. Fractionation purity analyzers and engineering models    -   4. Product quality lab data and engineering models        The invention also enables the operator to selectively remove        sensors from the models in the event that the sensor is out of        service and also provides suppression of model calculations to        eliminate false positives on special cause operations.        A. Operator Interface

The display is intended to give the operator a view of the probabilitythat there is an abnormal event affecting the process unit.

FIG. 23 shows the operator overview display of the HDC unit. Theoverview display is comprised of fifteen time-series plots (ormonitors). For each monitor there is at least one underlying model,either multivariable statistical models or engineering models. Eachmonitor contains a list of abnormal indications for the operational areaand uses a fuzzy network (described below) to aggregate abnormalindications. Based upon specific knowledge about the normal operationthe focus areas and functional areas, a fuzzy network was developed totake the input from sensors and model residuals to evaluate theprobability of an abnormal event. The estimated probability of anabnormal condition is displayed to the console operator on a continuoustime series plot to indicate the condition's evolution over time, asillustrated in FIG. 23. When the aggregate probability reaches aprescribed trigger (e.g. 0.6), the problem indicator turns yellow(warning) and the indicator turns red (alert) when the probabilityreaches a second trigger (e.g. 0.9).

This invention contains three Principle Component Analysis (PCA) modelsand numerous engineering models (described in more detail below) tocharacterize normal operation of the HDC. The boundaries for each of thePCA models was derived to account for heat and energy integrationbetween units rather than functional boundaries. To illustrate thispoint, the three sequential fixed bed reactors, R1 (hydrotreating), R2(hydrocracking) and R3 (hydrocracking), were modeled using 2 PCA models.The first PCA model encompasses R1 and R2 (and associated units). Thesecond PCA model contains R3 (and associated units). R1 & R2 were lumpedinto a single PCA model due to the recycle of recovered hydrogrenbetween the units. Similarly, the fractionation section (stabilizer andsplitter towers) was modeled using a single PCA model due to tightenergy integration (i.e. direct contact heat exchangers) between unitsrather than separate PCA models for each tower. In the preliminarydesign, separate PCA models for each tower were developed but werediscarded in favor of a single PCA model for the entire fractionationsection. Model validation studies indicated that the single PCA modelfor the fractionation section was more robust and better captured normaloperation of the process. For a more detailed description of the modelpartitioning methodology, see Subsection I.A under the heading“Developing PCA models for Abnormal Event Detection” of Appendix 1.

During normal operation of the HDC the console operator performs anumber of special cause operations, such as feed rate changes and recipcompressor LP discharge pressure changes, to balance inventories or tosteer the HDC to a preferred state. These special cause operations willproduce high residuals to some sensors in affected PCA models. Sincespecial cause operations are console operator initiated, this inventioncontains suppression methodologies to detect the onset of special causeoperations and to prevent notification of the operator. FIG. 29 showsthe fuzzy logic network for the first stage low pressure separatormonitor. For a more detailed description of PCA model implementation,see Subsection I under the heading “Deploying PCA models and SimpleEngineering Models for Abnormal Event Detection” of Appendix 1.

The console operator receives notification of the onset of an abnormalcondition when the triangle icon for a monitor turns yellow or red (fromgreen), as shown in FIG. 24. The application provides the operator withthe ability to further investigate the problem by viewing a prioritizedlist of the associated subproblems. Once the operator receives anindication of an abnormal condition, such as the warning alert indicatedby the yellow triangle in FIG. 2, this novel method provides theoperator with ability to investigate each subproblem to determine rootcause of the abnormality detected by the application. This functionalityis illustrated by FIG. 25. FIG. 25 demonstrates the presentation of alist of sensors organized in the form of a Pareto chart for presentationto the console operator.

This application frequently uses Pareto charts to organize informationfor presentation to the operator. As an example, FIG. 27 demonstrates aPareto ranking of the valve-flow models based onnormalized-projection-deviation error. By convention, the variable,measurement or sensor which most deviates from normal operation isplaced in the left-most position. When the root cause of an event cannotbe determined from the pareto chart, the operator can elect to furtherinvestigate by clicking on an individual bar from the chart. Thisoperation will typically generate either a custom display containingmultiple time series plots of the critical sensors of a functional areaof the HDC, shown in FIG. 26, or an x-y plot, shown in FIG. 28.

In summary, the advantages of this invention include:

-   -   1. The decomposition of the entire HDC operation into several        (e.g. 15) monitors for operator surveillance    -   2. Operator notification of abnormal operation of the entire HDC        through several (e.g. 15) monitors    -   3. The PCA models provide predictions of a large number of        sensors (greater than 300) in the HDC    -   4. The abnormal deviations of these large number of sensors are        summarized by the alerts derived from the Sum of Square Error of        the PCA models.    -   5. Events resulting from special cause operation are suppressed        to eliminate false positives. The high false positive rate of a        single sensor alarm is resolved by the PCA modeling.        B. Development of AED Models for a Hydrocracker

This application employs both PCA models and engineering models (andheuristics) to detect abnormal operation in an HDC. The overalldevelopment methodology of these models is generally described inAppendix 1. The development of models for hydrocracking unit isdescribed below.

Engineering Model Design

The engineering model requirements for the HDC application weredetermined by: performing an engineering evaluation of historicalprocess data from the HDC and interviews with console operators andequipment specialists. The engineering evaluation also included anevaluation of worst case scenarios for HDC operation. This processgenerated the following general conclusions:

-   -   The reactor quench system has a significant effect on safe and        reliable operation of the HDC    -   Detection of the onset of both stable and unstable sustained        temperature and pressure oscillations in the reactor beds is        required    -   Changes in feed quality (e.g. the percentage of coker naptha in        the feed) are significant upstream disturbances which impact the        hydrogen consumption and the reactor temperature profile    -   Focus areas for instrumentation and base control system:    -   Quench and hydrogen flow measurement integrity    -   HP/LP separator operation    -   Product quality analyzers    -   Monitoring compressors desireable but better accomplished with        higher frequency diagnostic systems

To address the conclusions from the engineering assessment, thefollowing engineering models were developed for the example HDC AEDapplication:

-   -   Flow/valve position consistency monitors    -   Quench demand estimation    -   Inferential estimates of product quality analyzers        -   nC4 in stabilizer gasoline draw        -   iC5 in stabilizer overhead        -   90% point (T90) kerosene draw    -   Inferential estimates of kersone product quality lab        measurements    -   Kero flash point temperature    -   Kero freeze point temperature    -   HP/LP separator monitors        -   Redundant level measurement cross check comparisons        -   Level measurement range check        -   Level measurment cycling detection        -   Frozen level measurement detection    -   Reactor stability monitor        -   Quench flow cycle detection        -   Bed temperature cycle detection        -   Total quench flow cycle detection        -   Reactor offgas oscillation detection

The flow/valve position consistency monitor was derived from acomparison of the measured flow (compensated for the pressure dropacross the valve) with a model estimate of the flow. The model estimateof the flow is obtained from historical data by fitting coefficients tothe valve curve equation (assumed to be either linear or parabolic). Inthe initial application, 37 flow/valve position consistency models weredeveloped. This type of model was developed for individual quench flowcontrol loops, all feed and product flow control loops, hydrogenrecovery flow control loops, fractionation reflux control loops, andfractionation bottoms flow control loops. Several models were alsodeveloped for control loops which historically exhibited unreliableperformance. A more detailed development of flow/valve consistencymonitors, see Subsection I.A of the “Simple Engineering Models forAbnormal Event Detection” section of Appendix 1.

A time-varying drift term was added to the model estimate to compensatefor long term sensor drift. The operator can also request a reset of thedrift term after a sensor recalibration or when a manual bypass valvehas been changed. This modification to the flow estimator significantlyimproved the robustness for implementation within an online detectionalgorithm. The flow/valve consistency monitors also notify the operatorin the event that a control valve is fully opened or closed. For a moredetailed description of compensation for non-stationary operations, seeSubsection IV.F of the “Developing PCA models for Abnormal EventDetection” section included in Appendix 1.

The inferential models for the product analyzers and lab measurementsare simple linear models fitted using partial least squares (PLS)regression. To improve the fit of the models, a number of well knownheuristics were employed including: log transformation of compositionanalyzers and tower overhead pressures, dynamic compensation of themodel inputs, conversion of flows to dimensionless ratios, and applyingpressure compensation to tray temperatures under VLE. The motivation forusing these types of transformations is summarized in Subsection IV.D inthe “Developing PCA models for Abnormal Event Detection” section ofAppendix 1

Loss of level in the HP/LP separators would cause a number ofsignificant issues for the example HDC unit, including trigger of thesafety systems. As a result, a fuzzy network was developed to monitorliquid level in the separator drums. For the example HDC unit, thefollowing four conditions are monitored: level measurement within range,level measurement active (not frozen), significant oscillation in thelevel measurement, and the primary level measurement agrees with back-upmeasurements (when available). The first two conditions can be detectedusing conventional signal validation algorithms in the DCS. Thisapplication incorporates additional criteria, including oscillationdetection, to provide the operator with more robust detection ofabnormal operation of the HP/LP separators. A summary of arepresentative configuration strategy for HP/LP monitoring is shown inTable 6.

Due to the highly exothermic nature of the hydrocracking reactions,stable operation of the fixed bed reactors is a significant concern forthe process operator. Cooling to the reactor beds is provided by quenchhydrogen. Since the quench hydrogen is typically provided to by a commonheader, a pressure oscillation in a single bed can trigger temperatureoscillations in several beds or reactors. The amplitude of theoscillation can be amplified downstream due to product transporteffects. For the example HDC unit, effective diagnosis of temperatureand pressure oscillations at the onset can provide the operator with anopportunity to intervene before the upset propagates to multiple beds(in which case more aggressive actions may be required of the operatorto mitigate the upset). This application uses a novel application offuzzy networks to monitor multiple temperature and flow indicators foroscillations to assess reactor stability. Within the reactor stabillitymonitor, instrumentation is inspected to determine if the amplitudeexceeds a certain threshold or the amplitude is increasing monotonicallyover a specified time horizon. R1 and R2 are monitored jointly due tocommon supply of recycled hydrogen. R3 is treated independently. This isconsistent with the partitioned treatment of the reactors in the PCAmodels. The amplitude triggers for each substituent measurement wereobtained by offline analysis of historical data. A summary of arepresentative set of reactor stability monitors is shown in Table 4 forR1R2 and Table 5 for R3.

PCA Model Design

PCA transforms the actual process variables into a set of independentvariables called Principal Components (PC) which are linear combinationsof the original variables. The PCA model structure is shown inEquation 1. It has been observed that the underlying process has anumber of degrees of freedom which represent the specific independenteffects that influence the process. These different independent effectsshow up in the process data as process variation. Process variation canbe due to intentional changes, such as feed rate changes, orunintentional disturbances, such as ambient temperature variation.PC _(i) =ΣA _(i,1) *X ₁ +A _(i,2) *X ₂ +A _(i,3) *X ₃+ . . .   Equation1

Each principal component captures a unique portion of the processvariability caused by these different independent influences on theprocess. The principal components are extracted in the order ofdecreasing process variation. Each subsequent principal componentcaptures a smaller portion of the total process variability. The majorprincipal components should represent significant underlying sources ofprocess variation. As an example, the first principal component oftenrepresents the effect of feed rate changes since this is usually thelargest single source of process changes.

For an individual principle component, the coefficients with the largestmagnitude are indicative of the measurements with the greatestcontibution to a particular PC. Engineering judgment can be utilized toview each group of variables which are the major contributors to a PCand assign a name indicating cause (e.g. feed rate effect) to that PC.For a further discussion of PCA models, see Subsections I-V of the“Developing PCA models for Abnormal Event Detection” section of Appendix1.

HDC Model Development

The initial application design involves two significant decisions: howto partition the example HDC unit for PCA model development anddetermination of engineering model requirements. Failure to correctlypartition the plant for PCA model development can have a significantimpact on the data requirements and the ability to adequately representnormal operation of the plant. The rationale for partitioning theexample HDC operation into 3 PCA models (lumped R1 & R2 reactors, R3reactor, and fractionation) has been discussed previously.

The PCA model developer makes two critical decisions to arrive at theintial model: 1) measurement selection and 2) data pre-processing (toremove outliers and “bad” values from the data set). The followingmethodology, condensed from Subsection II of the “Developing PCA modelsfor Abnormal Event Detection” Section of Appendix 1, was employed toselect the initial measurement set for this application:

-   -   Select all controller PV's, SP's and Outputs for streams which        cross the boundaries of the major processing units (e.g. quench        flow controllers, stabilizer bottoms flow rate, . . . )    -   Incorporate controller PV's, SP's and Outputs for internal        streams used by to position the unit (e.g. tower pumparounds,        reflux flows, . . . ) or monitor the process    -   Incorporate additional measurements used by contact engineers to        monitor process operation    -   Incorporate additional measurements regarded by process experts        as essential to monitor process operation    -   When available, incorporate:        -   Any upstream measurements of feedrate, feed temperature, or            indicators of feed quality changes        -   Any redundant measurements of critical instrumentation        -   Any external meaurements of a measured disturbance (e.g.            ambient temperature)        -   Select additional meaurements that may be required to            perform nonlinear tranformations            Any measurements that were known to be unreliable or exhibit            erratic behavior were also removed from the list.            Application of this selection methodology typically results            in the elimination of approximately 60% of the total            measurements from the plant for PCA model development.            Additional measurement reduction is performed using an            iterative procedure once the initial PCA model is obtained.            Data Pre-Processing

Development of a PCA model is an iterative procedure. The approachutilized to develop PCA models for AED initially produces a very roughmodel using all candidate measurements defined above. It is difficult toinitially remove all outliers since the initial training set to contain100,000+ data points. The initial model was used to evaluate thetraining data to eliminate additional outliers using the subsequentprocedure.

Using the operating logs, data contained within windows with known unitshutdowns or abnormal operation were removed. Each candidate measurementwas scrutinized to determine appropriateness for inclusion in thetraining data set. Measurements which were excluded exhibited thefollowing characteristics (described in Subsection III.A of the“Developing PCA models for Abnormal Event Detection” section of Appendix1):

-   -   Long periods of time which the historian has labeled the data as        “BAD PV”    -   Occupy excessive periods of time at either the EUHigh or EULow        value    -   Show unusually low variability (except measurements which are        tightly controlled to a setpoint)    -   Excessive noise or high variability relative to their operating        range        Measurements which exhibited low signal to noise ratio or poor        correlation with other related measurements were omitted using        engineering judgment.

Before building the initial rough PCA model, a final inspection of thedata set was performed to eliminate brief time periods where themeasurements contained “BAD PV” or were pegged to their respectiveEUHigh or EULow limits.

For a number of reasons (summarized in Subsection IV.D in the“Developing PCA models for Abnormal Event Detection” section of Appendix1), well known transformations were applied to individual measurements.Since one of the assumptions of PCA is that the variables in the modelare linearly correlated, significant process or equipment nonlinearitiesbreak down this correlation structure and show up as a deviation fromthe model. Based upon an engineering assessment of the specific processequiment and process chemistry, known nonlinearities in the process weretransformed and included in the model. Examples of well known nonlineartransforms include:

-   -   Reflux to feed ratio in distillation columns    -   Reactor bed differential temperatures and pressures    -   Log of composition in high purity distillation columns    -   Pressure compensated temperature measurements    -   Reaction rate to exponential temperature

The raw data from the data historian was nonstationary. The datacontained operating point changes performed by the console operator. Toprevent these changes from appearing as abnormal events, the data wasconverted to deviation variables by subtracting the exponentiallyfiltered value of a measurement from its raw value and using thisdifference in the model. The time constant for the exponential filtershould be large (on the order of the dominant process time constant).For the example HDC unit, a 60 minute time constant was utilized. For amore detailed description of AED applications for nonstationaryprocesses, see Subsection IV.F of the “Developing PCA models forAbnormal Event Detection” section of Appendix 1.

Ideally, the training data set should characterize all normal excitationand disturbances that the process experiences. This can be frequentlyaccomplished by gathering data over a sufficiently long period of time(several months or a year). If a single type of disturbance dominatesthe training data, other modes of process operation will beunderrepresented and the resultant models will not achieve the desiredperformance. It may be necessary to remove data from the training set toprevent this situation from occuring.

Building an Initial Model

Once the specific measurements have been selected and the training dataset has been built, the model can be built quickly using standard tools.An implicit assumption is that each measurement will be scaled to unitvariance prior to obtaining the model coefficients. Many of the standardtools scale the variables automatically.

It is not important for the developer to scrutinize the number ofprinciple components in the initial model. Typically the model developercan specify a default number of principle components (e.g. 1 principlecomponent for every 20 measurements in the data set) and attempt toidentify a model that produces the minimal residual error from thetraining data set.

The initial model is used by the developer for two purposes: 1) toidentify problematic regions in the training data set, and 2) toeliminate unneccessary measurements from the training data set.

Since the training data sets are typically large (1 minute data samplesfor up to 1 year), it is unlikely that all outliers will be eliminatedfrom the training data in the data preprocessing step. Regions withlarge model mismatch between the training data and the prediction fromthe initial model should be identified and compared with operating logsto determine if an abnormal operation was occuring in the process atthat time. The training data set should be modified to exclude regionsin which the developer believes an abnormal event was occuring.Discretion must be used by the developer to ensure that the validity ofthe training data set is preserved. Additional discussion is provided inSubsection II.A of the “Developing PCA models for Abnormal EventDetection” section of Appendix 1.

The measurement selection process typically produces a comprehensive setof sensors. Analysis of the scaled model coefficients was used toeliminate approximately 20% of the measurements from the training set.Engineering judgment should be used to determine which measurements toeliminate from the training data set.

Refining the Model

The objective is to identify a PCA model which suitably represents thetraining data with the minimal number of coefficients. It has beendemonstrated in the literature that “overfitting” the training data(using more principle components or sensors than required) can produce amodel that is not suitable for application in an online system such asAED. An iterative procedure was used to produce models with a suitablenumber of principle components. The iterative procedure considered theamount of variation modeled by successive principle components(calculated by a standard statistical software package) and the modelresidual.

C. Model Implementation

Successful deployment of AED on a process unit requires a combination ofaccurate models, a well designed user interface and proper triggerpoints.

Engineering Model Deployment

The procedure for implementing the engineering models within AED isfairly straightforward. For the models which identify specific knowntypes of behavior within the unit (e.g. sustained oscillation of reactorbed temperature & pressure, LP/HP separator operation), the triggerpoints for notification were determined from the analysis of historicaldata in combination with console operator input. For the computationalmodels (e.g. flow/valve position models, inferred analyzer comparison),the trigger points for notification were initially derived from thestandard deviation of the model residual. For the first several monthsof operation, known AED indications were reviewed with the operator toensure that the trigger points were appropriate and modified asnecessary.

Under certain circumstances, the valve/flow diagnostics could providethe operator with redundant notification. Model suppression was appliedto the valve/flow diagnostics to provide the operator with a singlealert to a problem with a valve/flow pair.

PCA Model Deployment

Variation in the process data does not typically have a normal orgaussian distribution. As a result, the trigger for detecting anabnormal event cannot be derived from the variability of the residualerror. Some rules of thumb have been developed for AED to obtain initialvalues for the triggers from the SPE_(x) statistic for the training dataset (also referred to as the Q statistic or the DMOD_(x) statistic).This guideline was developed to provide reasonable confidence that theconsole operator will be alerted to true abnormal events without beingoverwhelmed by false alarms. For additional details, refer to SubsectionVI of the “Developing PCA models for Abnormal Event Detection” sectionof Appendix 1.

Over time, the developer or site engineer may determine that it isnecessary to improve one of the models. Either the process conditionshave changed or the model is providing a false indication. In thisevent, the training data set could be augmented with additional processdata and improved model coefficients could be obtained. The triggerpoints can be recalculated using the same rules of thumb mentionedpreviously.

Old data that no longer adequately represents process operations shouldbe removed from the training data set. If a particular type of operationis no longer being done, all data from that operation should be removed.After a major process modification, the training data and AED model mayneed to be rebuilt from scratch.

Appendix 1

Events and disturbances of various magnitudes are constantly affectingprocess operations. Most of the time these events and disturbances arehandled by the process control system. However, the operator is requiredto make an unplanned intervention in the process operations whenever theprocess control system cannot adequately handle the process event. Wedefine this situation as an abnormal operation and the cause defined asan abnormal event.

A methodology and system has been developed to create and to deployon-line, sets of models, which are used to detect abnormal operationsand help the operator isolate the location of the root cause. In apreferred embodiment, the models employ principle component analysis(PCA). These sets of models are composed of both simple models thatrepresent known engineering relationships and principal componentanalysis (PCA) models that represent normal data patterns that existwithin historical databases. The results from these many modelcalculations are combined into a small number of summary time trendsthat allow the process operator to easily monitor whether the process isentering an abnormal operation.

FIG. 1 shows how the information in the online system flows through thevarious transformations, model calculations, fuzzy Petri nets andconsolidations to arrive at a summary trend which indicates thenormality/abnormality of the process areas. The heart of this system isthe various models used to monitor the normality of the processoperations.

The PCA models described in this invention are intended to broadlymonitor continuous refining and chemical processes and to rapidly detectdeveloping equipment and process problems. The intent is to provideblanket monitoring of all the process equipment and process operationsunder the span of responsibility of a particular console operator post.This can involve many major refining or chemical process operating units(e.g. distillation towers, reactors, compressors, heat exchange trains,etc.) which have hundreds to thousands of process measurements. Themonitoring is designed to detect problems which develop on a minutes tohours timescale, as opposed to long term performance degradation. Theprocess and equipment problems do not need to be specified beforehand.This is in contrast to the use of PCA models cited in the literaturewhich are structured to detect a specific important process problem andto cover a much smaller portion of the process operations.

To accomplish this objective, the method for PCA model development anddeployment includes a number of novel extensions required for theirapplication to continuous refining and chemical processes including:

-   -   criteria for establishing the equipment scope of the PCA models        criteria and methods for selecting, analyzing, and transforming        measurement inputs    -   developing of multivariate statistical models based on a        variation of principle component models, PCA    -   developing models based on simple engineering relationships        restructuring the associated statistical indices    -   preprocessing the on-line data to provide exception calculations        and continuous on-line model updating    -   using fuzzy Petri nets to interpret model indices as normal or        abnormal    -   using fuzzy Petri nets to combine multiple model outputs into a        single continuous summary indication of normality/abnormality        for a process area    -   design of operator interactions with the models and fuzzy Petri        nets to reflect operations and maintenance activities

These extensions are necessary to handle the characteristics ofcontinuous refining and chemical plant operations and the correspondingdata characteristics so that PCA and simple engineering models can beused effectively. These extensions provide the advantage of preventingmany of the Type I and Type II errors and quicker indications ofabnormal events.

This section will not provide a general background to PCA. For that,readers should refer to a standard textbook such as E. Jackson's “AUser's Guide to Principal Component Analysis” (2)

The classical PCA technique makes the following statistical assumptionsall of which are violated to some degree by the data generated fromnormal continuous refining and chemical plant process operations:

-   -   1. The process is stationary—its mean and variance are constant        over time.    -   2. The cross correlation among variables is linear over the        range of normal process operations    -   3. Process noise random variables are mutually independent.    -   4. The covariance matrix of the process variables is not        degenerate (i.e. positive semi-definite).    -   5. The data are scaled “appropriately” (the standard statistical        approach being to scale to unit variance).    -   6. There are no (uncompensated) process dynamics (a standard        partial compensation for this being the inclusion of lag        variables in the model)    -   7. All variables have some degree of cross correlation.    -   8. The data have a multivariate normal distribution

Consequently, in the selection, analysis and transformation of inputsand the subsequent in building the PCA model, various adjustments aremade to evaluate and compensate for the degree of violation.

Once these PCA models are deployed on-line the model calculationsrequire specific exception processing to remove the effect of knownoperation and maintenance activities, to disable failed or “bad acting”inputs, to allow the operator observe and acknowledge the propagation ofan event through the process and to automatically restore thecalculations once the process has returned to normal.

Use of PCA models is supplemented by simple redundancy checks that arebased on known engineering relationships that must be true during normaloperations. These can be as simple as checking physically redundantmeasurements, or as complex as material and engineering balances.

The simplest form of redundancy checks are simple 2×2 checks, e.g.

-   -   temperature 1=temperature 2    -   flow 1=valve characteristic curve 1 (valve 1 position)    -   material flow into process unit 1=material flow out of process        unit 1

These are shown to the operator as simple x-y plots, such as the valveflow plot in FIG. 2. Each plot has an area of normal operations, shownon this plot by the gray area. Operations outside this area are signaledas abnormal.

Multiple redundancy can also be checked through a singlemultidimensional model. Examples of multidimensional redundancy are:

-   -   pressure 1=pressure 2= . . . . =pressure n    -   material flow into process unit 1=material flow out of process        unit 1= . . . =material flow into process unit 2

Multidimensional checks are represented with “PCA like” models. In FIG.3, there are three independent and redundant measures, X1, X2, and X3.Whenever X3 changes by one, X1 changes by a₁₃ and X2 changes by a₂₃.This set of relationships is expressed as a PCA model with a singleprinciple component direction, P. This type of model is presented to theoperator in a manner similar to the broad PCA models. As with the twodimensional redundancy checks the gray area shows the area of normaloperations. The principle component loadings of P are directlycalculated from the engineering equations, not in the traditional mannerof determining P from the direction of greatest variability.

The characteristics of the process operation require exceptionoperations to keep these relationships accurate over the normal range ofprocess operations and normal field equipment changes and maintenanceactivities. Examples of exception operations are:

-   -   opening of bypass valves around flow meters    -   compensating for upstream/downstream pressure changes    -   recalibration of field measurements    -   redirecting process flows based on operating modes

The PCA models and the engineering redundancy checks are combined usingfuzzy Petri nets to provide the process operator with a continuoussummary indication of the normality of the process operations under hiscontrol (FIG. 4).

Multiple statistical indices are created from each PCA model so that theindices correspond to the configuration and hierarchy of the processequipment that the process operator handles. The sensitivity of thetraditional sum of Squared Prediction Error, SPE, index is improved bycreating subset indices, which only contain the contribution to the SPEindex for the inputs which come from designated portions of the completeprocess area covered by the PCA model. Each statistical index from thePCA models is fed into a fuzzy Petri net to convert the index into azero to one scale, which continuously indicates the range from normaloperation (value of zero) to abnormal operation (value of one).

Each redundancy check is also converted to a continuous normal abnormalindication using fuzzy nets. There are two different indices used forthese models to indicate abnormality; deviation from the model anddeviation outside the operating range (shown on FIG. 3). Thesedeviations are equivalent to the sum of the square of the error and theHotelling T square indices for PCA models. For checks with dimensiongreater than two, it is possible to identify which input has a problem.In FIG. 3, since the X3-X2 relationship is still within the normalenvelope, the problem is with input X1. Each deviation measure isconverted by the fuzzy Petri net into a zero to one scale that willcontinuously indicate the range from normal operation (value of zero) toabnormal operation (value of one).

For each process area under the authority of the operator, theapplicable set of normal-abnormal indicators is combined into a singlenormal-abnormal indicator. This is done by using fuzzy Petri logic toselect the worst case indication of abnormal operation. In this way theoperator has a high level summary of all the checks within the processarea. This section will not provide a general background to fuzzy Petrinets. For that, readers should refer to Cardoso, et al, Fuzzy PetriNets: An Overview (1)

The overall process for developing an abnormal event application isshown in FIG. 5. The basic development strategy is iterative where thedeveloper starts with a rough model, then successively improves thatmodel's capability based on observing how well the model represents theactual process operations during both normal operations and abnormaloperations. The models are then restructured and retrained based onthese observations.

Developing PCA Models for Abnormal Event Detection

I. Conceptual PCA Model Design

The overall design goals are to:

-   -   provide the console operator with a continuous status (normal        vs. abnormal) of process operations for all of the process units        under his operating authority    -   provide him with an early detection of a rapidly developing        (minutes to hours) abnormal event within his operating authority    -   provide him with only the key process information needed to        diagnose the root cause of the abnormal event.

Actual root cause diagnosis is outside the scope of this invention. Theconsole operator is expected to diagnosis the process problem based onhis process knowledge and training.

Having a broad process scope is important to the overall success ofabnormal operation monitoring. For the operator to learn the system andmaintain his skills, he needs to regularly use the system. Sincespecific abnormal events occur infrequently, abnormal operationsmonitoring of a small portion of the process would be infrequently usedby the operator, likely leading the operator to disregard the systemwhen it finally detects an abnormal event. This broad scope is incontrast to the published modeling goal which is to design the modelbased on detecting a specific process problem of significant economicinterest (see Kourti, 2004).

There are thousands of process measurements within the process unitsunder a single console operator's operating authority. Continuousrefining and chemical processes exhibit significant time dynamics amongthese measurements, which break the cross correlation among the data.This requires dividing the process equipment into separate PCA modelswhere the cross correlation can be maintained.

Conceptual model design is composed of four major decisions:

-   -   Subdividing the process equipment into equipment groups with        corresponding PCA models    -   Subdividing process operating time periods into process        operating modes requiring different PCA models    -   Identifying which measurements within an equipment group should        be designated as inputs to each PCA model    -   Identifying which measurements within an equipment group should        act as flags for suppressing known events or other exception        operations        A. Process Unit Coverage

The initial decision is to create groups of equipment that will becovered by a single PCA model. The specific process units includedrequires an understanding of the process integration/interaction.Similar to the design of a multivariable constraint controller, theboundary of the PCA model should encompass all significant processinteractions and key upstream and downstream indications of processchanges and disturbances.

The following rules are used to determined these equipment groups:

Equipment groups are defined by including all the major material andenergy integrations and quick recycles in the same equipment group. Ifthe process uses a multivariable constraint controller, the controllermodel will explicitly identify the interaction points among the processunits. Otherwise the interactions need to be identified through anengineering analysis of the process.

Process groups should be divided at a point where there is a minimalinteraction between the process equipment groups. The most obviousdividing point occurs when the only interaction comes through a singlepipe containing the feed to the next downstream unit. In this case thetemperature, pressure, flow, and composition of the feed are the primaryinfluences on the downstream equipment group and the pressure in theimmediate downstream unit is the primary influence on the upstreamequipment group. These primary influence measurements should be includedin both the upstream and downstream equipment group PCA models.

Include the influence of the process control applications betweenupstream and downstream equipment groups. The process controlapplications provide additional influence paths between upstream anddownstream equipment groups. Both feedforward and feedback paths canexist. Where such paths exist the measurements which drive these pathsneed to be included in both equipment groups. Analysis of the processcontrol applications will indicate the major interactions among theprocess units.

Divide equipment groups wherever there are significant time dynamics(e.g. storage tanks, long pipelines etc.). The PCA models primarilyhandle quick process changes (e.g. those which occur over a period ofminutes to hours). Influences, which take several hours, days or evenweeks to have their effect on the process, are not suitable for PCAmodels. Where these influences are important to the normal datapatterns, measurements of these effects need to be dynamicallycompensated to get their effect time synchronized with the other processmeasurements (see the discussion of dynamic compensation).

B. Process Operating Modes

Process operating modes are defined as specific time periods where theprocess behavior is significantly different. Examples of these areproduction of different grades of product (e.g. polymer production),significant process transitions (e.g. startups, shutdowns, feedstockswitches), processing of dramatically different feedstock (e.g. crackingnaphtha rather than ethane in olefins production), or differentconfigurations of the process equipment (different sets of process unitsrunning).

Where these significant operating modes exist, it is likely thatseparate PCA models will need to be developed for each major operatingmode. The fewer models needed the better. The developer should assumethat a specific PCA model could cover similar operating modes. Thisassumption must be tested by running new data from each operating modethrough the model to see if it behaves correctly.

C. Historical Process Problems

In order for there to be organizational interest in developing anabnormal event detection system, there should be an historical processproblem of significant economic impact. However, these significantproblems must be analyzed to identify the best approach for attackingthese problems. In particular, the developer should make the followingchecks before trying to build an abnormal event detection application:

-   1. Can the problem be permanently fixed? Often a problem exists    because site personnel have not had sufficient time to investigate    and permanently solve the problem. Once the attention of the    organization is focused on the problem, a permanent solution is    often found. This is the best approach.-   2. Can the problem be directly measured? A more reliable way to    detect a problem is to install sensors that can directly measure the    problem in the process. This can also be used to prevent the problem    through a process control application. This is the second best    approach.-   3. Can an inferential measurement be developed which will measure    the approach to the abnormal operation? Inferential measurements are    usually developed using partial least squares, PLS, models which are    very close relatives to PCA abnormal event models. Other common    alternatives for developing inferential measurements include Neural    Nets and linear regression models. If the data exists which can be    used to reliably measure the approach to the problem condition (e.g.    tower flooding using delta pressure), this can then be used to not    only detect when the condition exists but also as the base for a    control application to prevent the condition from occurring. This is    the third best approach.

Both direct measurements of problem conditions and inferentialmeasurements of these conditions can be easily integrated into theoverall network of abnormal detection models.

II. Input Data and Operating Range Selection

Within an equipment group, there will be thousands of processmeasurements. For the preliminary design:

-   -   Select all cascade secondary controller measurements, and        especially ultimate secondary outputs (signals to field control        valves) on these units    -   Select key measurements used by the console operator to monitor        the process (e.g. those which appear on his operating        schematics)    -   Select any measurements used by the contact engineer to measure        the performance of the process    -   Select any upstream measurement of feedrate, feed temperature or        feed quality    -   Select measurements of downstream conditions which affect the        process operating area, particularly pressures.    -   Select extra redundant measurements for measurements that are        important    -   Select measurements that may be needed to calculate non-linear        transformations.    -   Select any external measurement of a disturbance (e.g. ambient        temperature)    -   Select any other measurements, which the process experts regard        as important measures of the process condition

From this list only include measurements which have the followingcharacteristics:

-   -   The measurement does not have a history of erratic or problem        performance    -   The measurement has a satisfactory signal to noise ratio    -   The measurement is cross-correlated with other measurements in        the data set    -   The measurement is not saturated for more than 10% of the time        during normal operations.    -   The measurement is not tightly controlled to a fixed setpoint,        which rarely changes (the ultimate primary of a control        hierarchy).    -   The measurement does not have long stretches of “Bad Value”        operation or saturated against transmitter limits.    -   The measurement does not go across a range of values, which is        known to be highly non-linear    -   The measurement is not a redundant calculation from the raw        measurements    -   The signals to field control valves are not saturated for more        than 10% of the time        A. Evaluations for Selecting Model Inputs

There are two statistical criteria for prioritizing potential inputsinto the PCA Abnormal Detection Model, Signal to Noise Ratio andCross-Correlation.

1) Signal to Noise Test

The signal to noise ratio is a measure of the information content in theinput signal.

The signal to noise ratio is calculated as follows:

-   1. The raw signal is filtered using an exponential filter with an    approximate dynamic time constant equivalent to that of the process.    For continuous refining and chemical processes this time constant is    usually in the range of 30 minutes to 2 hours. Other low pass    filters can be used as well. For the exponential filter the    equations are:    Y _(n) =P*Y _(n−1)+(1−P)*X _(n) Exponential filter    equation  Equation 1    P=Exp(−T _(s) /T _(f)) Filter constant calculation  Equation 2-    where:    -   Y_(n) the current filtered value    -   Y_(n−1) the previous filtered value    -   X_(n) the current raw value    -   P the exponential filter constant    -   T_(s) the sample time of the measurement    -   T_(f) the filter time constant-   2. A residual signal is created by subtracting the filtered signal    from the raw signal    R _(n) =X _(n) −Y _(n)  Equation 3-   3. The signal to noise ratio is the ratio of the standard deviation    of the filtered signal divided by the standard deviation of the    residual signal    S/N=σ _(Y)/σ_(R)  Equation 4

It is preferable to have all inputs exhibit a S/N which is greater thana predetermined minimum, such as 4. Those inputs with S/N less than thisminimum need individual examination to determine whether they should beincluded in the model

The data set used to calculate the S/N should exclude any long periodsof steady-state operation since that will cause the estimate for thenoise content to be excessively large.

2) Cross Correlation Test

The cross correlation is a measure of the information redundancy theinput data set. The cross correlation between any two signals iscalculated as:

-   1. Calculate the co-variance, S_(ik), between each input pair, i and    k $\begin{matrix}    {S_{ik} = \frac{{N*{\Sigma\left( {X_{i}*X_{k}} \right)}} - {\left( {\Sigma\quad X_{i}} \right)*\left( {\Sigma\quad X_{k}} \right)}}{N*\left( {N - 1} \right)}} & {{Equation}\quad 5}    \end{matrix}$-   2. Calculate the correlation coefficient for each pair of inputs    from the co-variance:    CC _(ik) =S _(ik)/(S_(ii) *S _(kk))^(1/2)  Equation 6

There are two circumstances, which flag that an input should not beincluded in the model. The first circumstance occurs when there is nosignificant correlation between a particular input and the rest of theinput data set. For each input, there must be at least one other inputin the data set with a significant correlation coefficient, such as 0.4.

The second circumstance occurs when the same input information has been(accidentally) included twice, often through some calculation, which hasa different identifier. Any input pairs that exhibit correlationcoefficients near one (for example above 0.95) need individualexamination to determine whether both inputs should be included in themodel. If the inputs are physically independent but logically redundant(i.e., two independent thermocouples are independently measuring thesame process temperature) then both these inputs should be included inthe model.

If two inputs are transformations of each other (i.e., temperature andpressure compensated temperature) the preference is to include themeasurement that the operator is familiar with, unless there is asignificantly improved cross correlation between one of thesemeasurements and the rest of the dataset. Then the one with the highercross correlation should be included.

3) Identifying & Handling Saturated Variables

Refining and chemical processes often run against hard and softconstraints resulting in saturated values and “Bad Values” for the modelinputs. Common constraints are: instrument transmitter high and lowranges, analyzer ranges, maximum and minimum control valve positions,and process control application output limits. Inputs can fall intoseveral categories with regard to saturation which require specialhandling when pre-processing the inputs, both for model building and forthe on-line use of these models.

Bad Values

13

For standard analog instruments (e.g., 4-20 milliamp electronictransmitters), bad values can occur because of two separate reasons:

-   -   The actual process condition is outside the range of the field        transmitter    -   The connection with the field has been broken

When either of these conditions occur, the process control system couldbe configured on an individual measurement basis to either assign aspecial code to the value for that measurement to indicate that themeasurement is a Bad Value, or to maintain the last good value of themeasurement. These values will then propagate throughout anycalculations performed on the process control system. When the “lastgood value” option has been configured, this can lead to erroneouscalculations that are difficult to detect and exclude. Typically whenthe “Bad Value” code is propagated through the system, all calculationswhich depend on the bad measurement will be flagged bad as well.

Regardless of the option configured on the process control system, thosetime periods, which include Bad Values should not be included intraining or test data sets. The developer needs to identify which optionhas been configured in the process control system and then configuredata filters for excluding samples, which are Bad Values. For theon-line implementation, inputs must be pre-processed so that Bad Valuesare flagged as missing values, regardless of which option had beenselected on the process control system.

Those inputs, which are normally Bad Value for extensive time periodsshould be excluded from the model.

Constrained Variables

Constrained variables are ones where the measurement is at some limit,and this measurement matches an actual process condition (as opposed towhere the value has defaulted to the maximum or minimum limit of thetransmitter range—covered in the Bad Value section). This processsituation can occur for several reasons:

-   -   Portions of the process are normally inactive except under        special override conditions, for example pressure relief flow to        the flare system. Time periods where these override conditions        are active should be excluded from the training and validation        data set by setting up data filters. For the on-line        implementation these override events are trigger events for        automatic suppression of selected model statistics    -   The process control system is designed to drive the process        against process operating limits, for example product spec        limits. These constraints typically fall into two        categories:—those, which are occasionally saturated and those,        which are normally saturated. Those inputs, which are normally        saturated, should be excluded from the model. Those inputs,        which are only occasionally saturated (for example less than 10%        of the time) can be included in the model however, they should        be scaled based on the time periods when they are not saturated.        B. Input from Process Control Applications

The process control applications have a very significant effect on thecorrelation structure of the process data. In particular:

-   -   The variation of controlled variables is significantly reduced        so that movement in the controlled variables is primarily noise        except for those brief time periods when the process has been        hit with a significant process disturbance or the operator has        intentionally moved the operating point by changing key        setpoints.    -   The normal variation in the controlled variables is transferred        by the control system to the manipulated variables (ultimately        the signals sent to the control valves in the field).

The normal operations of refinery and chemical processes are usuallycontrolled by two different types of control structures: the classicalcontrol cascades (shown in FIG. 6) and the more recent multivariableconstraint controllers, MVCC (shown in FIG. 7).

1) Selecting Model Inputs from Cascade Structures

FIG. 6 shows a typical “cascade” process control application, which is avery common control structure for refining and chemical processes.Although there are many potential model inputs from such an application,the only ones that are candidates for the model are the raw processmeasurements (the “PVs” in this figure) and the final output to thefield valve.

Although it is a very important measurement, the PV of the ultimateprimary of the cascade control structure is a poor candidate forinclusion in the model. This measurement usually has very limitedmovement since the objective of the control structure is to keep thismeasurement at the setpoint. There can be movement in the PV of theultimate primary if its setpoint is changed but this usually isinfrequent. The data patterns from occasional primary setpoint moveswill usually not have sufficient power in the training dataset for themodel to characterize the data pattern.

Because of this difficulty in characterizing the data pattern resultingfrom changes in the setpoint of the ultimate primary, when the operatormakes this setpoint move, it is likely to cause a significant increasein the sum of squared prediction error, SPE, index of the model.Consequently, any change in the setpoint of the ultimate primary is acandidate trigger for a “known event suppression”. Whenever the operatorchanges an ultimate primary setpoint, the “known event suppression”logic will automatically remove its effect from the SPE calculation.

Should the developer include the PV of the ultimate primary into themodel, this measurement should be scaled based on those brief timeperiods during which the operator has changed the setpoint and until theprocess has moved close to the vale of the new setpoint (for examplewithin 95% of the new setpoint change thus if the setpoint change isfrom 10 to 11, when the PV reaches 10.95)

There may also be measurements that are very strongly correlated (forexample greater than 0.95 correlation coefficient) with the PV of theUltimate Primary, for example redundant thermocouples located near atemperature measurement used as a PV for an Ultimate Primary. Theseredundant measurements should be treated in the identical manner that ischosen for the PV of the Ultimate Primary.

Cascade structures can have setpoint limits on each secondary and canhave output limits on the signal to the field control valve. It isimportant to check the status of these potentially constrainedoperations to see whether the measurement associated with a setpoint hasbeen operated in a constrained manner or whether the signal to the fieldvalve has been constrained. Date during these constrained operationsshould not be used.

2) Selecting/Calculating Model Inputs from Multivariable ConstraintControllers, MVCC

FIG. 7 shows a typical MVCC process control application, which is a verycommon control structure for refining and chemical processes. An MVCCuses a dynamic mathematical model to predict how changes in manipulatedvariables, MVs, (usually valve positions or setpoints of regulatorycontrol loops) will change control variables, CVs (the dependenttemperatures, pressures, compositions and flows which measure theprocess state). An MVCC attempts to push the process operation againstoperating limits. These limits can be either MV limits or CV limits andare determined by an external optimizer. The number of limits that theprocess operates against will be equal to the number of MVs thecontroller is allowed to manipulate minus the number of materialbalances controlled. So if an MVCC has 12 MVs, 30 CVs and 2 levels thenthe process will be operated against 10 limits. An MVCC will alsopredict the effect of measured load disturbances on the process andcompensate for these load disturbances (known as feedforward variables,FF).

Whether or not a raw MV or CV is a good candidate for inclusion in thePCA model depends on the percentage of time that MV or CV is heldagainst its operating limit by the MVCC. As discussed in the ConstrainedVariables section, raw variables that are constrained more than 10% ofthe time are poor candidates for inclusion in the PCA model. Normallyunconstrained variables should be handled per the Constrained Variablessection discussion.

If an unconstrained MV is a setpoint to a regulatory control loop, thesetpoint should not be included, instead the measurement of thatregulatory control loop should be included. The signal to the fieldvalve from that regulatory control loop should also be included.

If an unconstrained MV is a signal to a field valve position, then itshould be included in the model.

C. Redundant Measurements

The process control system databases can have a significant redundancyamong the candidate inputs into the PCA model. One type of redundancy is“physical redundancy”, where there are multiple sensors (such asthermocouples) located in close physical proximity to each other withinthe process equipment. The other type of redundancy is “calculationalredundancy”, where raw sensors are mathematically combined into newvariables (e.g. pressure compensated temperatures or mass flowscalculated from volumetric flow measurements).

As a general rule, both the raw measurement and an input which iscalculated from that measurement should not be included in the model.The general preference is to include the version of the measurement thatthe process operator is most familiar with. The exception to this ruleis when the raw inputs must be mathematically transformed in order toimprove the correlation structure of the data for the model. In thatcase the transformed variable should be included in the model but notthe raw measurement.

Physical redundancy is very important for providing cross validationinformation in the model. As a general rule, raw measurements, which arephysically redundant should be included in the model. When there are alarge number of physically redundant measurements, these measurementsmust be specially scaled so as to prevent them from overwhelming theselection of principle components (see the section on variable scaling).A common process example occurs from the large number of thermocouplesthat are placed in reactors to catch reactor runaways.

When mining a very large database, the developer can identify theredundant measurements by doing a cross-correlation calculation amongall of the candidate inputs. Those measurement pairs with a very highcross-correlation (for example above 0.95) should be individuallyexamined to classify each pair as either physically redundant orcalculationally redundant.

III. Historical Data Collection

A significant effort in the development lies in creating a good trainingdata set, which is known to contain all modes of normal processoperations. This data set should:

Span the normal operating range: Datasets, which span small parts of theoperating range, are composed mostly of noise. The range of the datacompared to the range of the data during steady state operations is agood indication of the quality of the information in the dataset.

Include all normal operating modes (including seasonal mode variations).Each operating mode may have different correlation structures. Unlessthe patterns, which characterize the operating mode, are captured by themodel, these unmodeled operating modes will appear as abnormaloperations.

Only include normal operating data: If strong abnormal operating data isincluded in the training data, the model will mistakenly model theseabnormal operations as normal operations. Consequently, when the modelis later compared to an abnormal operation, it may not detect theabnormality operations.

History should be as similar as possible to the data used in the on-linesystem: The online system will be providing spot values at a frequencyfast enough to detect the abnormal event. For continuous refining andchemical operations this sampling frequency will be around one minute.Within the limitations of the data historian, the training data shouldbe as equivalent to one-minute spot values as possible.

The strategy for data collection is to start with a long operatinghistory (usually in the range of 9 months to 18 months), then try toremove those time periods with obvious or documented abnormal events. Byusing such a long time period,

-   -   the smaller abnormal events will not appear with sufficient        strength in the training data set to significantly influence the        model parameters    -   most operating modes should have occurred and will be        represented in the data.        A. Historical Data Collection Issues        1) Data Compression

Many historical databases use data compression to minimize the storagerequirements for the data. Unfortunately, this practice can disrupt thecorrelation structure of the data. At the beginning of the project thedata compression of the database should be turned off and the spotvalues of the data historized. Final models should be built usinguncompressed data whenever possible. Averaged values should not be usedunless they are the only data available, and then with the shortest dataaverage available.

2) Length of Data History

For the model to properly represent the normal process patterns, thetraining data set needs to have examples of all the normal operatingmodes, normal operating changes and changes and normal minordisturbances that the process experiences. This is accomplished by usingdata from over a long period of process operations (e.g. 9-18 months).In particular, the differences among seasonal operations (spring,summer, fall and winter) can be very significant with refinery andchemical processes.

Sometimes these long stretches of data are not yet available (e.g. aftera turnaround or other significant reconfiguration of the processequipment). In these cases the model would start with a short initialset of training data (e.g. 6 weeks) then the training dataset isexpanded as further data is collected and the model updated monthlyuntil the models are stabilized (e.g. the model coefficients don'tchange with the addition of new data)

3) Ancillary Historical Data

The various operating journals for this time period should also becollected. This will be used to designate operating time periods asabnormal, or operating in some special mode that needs to be excludedfrom the training dataset. In particular, important historical abnormalevents can be selected from these logs to act as test cases for themodels.

4) Lack of Specific Measurement History

Often setpoints and controller outputs are not historized in the plantprocess data historian. Historization of these values should immediatelybegin at the start of the project.

5) Operating Modes

Old data that no longer properly represents the current processoperations should be removed from the training data set. After a majorprocess modification, the training data and PCA model may need to berebuilt from scratch. If a particular type of operation is no longerbeing done, all data from that operation should be removed from thetraining data set.

Operating logs should be used to identify when the process was run underdifferent operating modes. These different modes may require separatemodels. Where the model is intended to cover several operating modes,the number of samples in the training dataset from each operating modelshould be approximately equivalent.

6) Sampling Rate

The developer should gather several months of process data using thesite's process historian, preferably getting one minute spot values. Ifthis is not available, the highest resolution data, with the leastamount of averaging should be used.

7) Infrequently Sampled Measurements

Quality measurements (analyzers and lab samples) have a much slowersample frequency than other process measurements, ranging from tens ofminutes to daily. In order to include these measurements in the model acontinuous estimate of these quality measurements needs to beconstructed. FIG. 8 shows the online calculation of a continuous qualityestimate. This same model structure should be created and applied to thehistorical data. This quality estimate then becomes the input into thePCA model.

8) Model Triggered Data Annotation

Except for very obvious abnormalities, the quality of historical data isdifficult to determine. The inclusion of abnormal operating data canbias the model. The strategy of using large quantities of historicaldata will compensate to some degree the model bias caused by abnormaloperating in the training data set. The model built from historical datathat predates the start of the project must be regarded with suspicionas to its quality. The initial training dataset should be replaced witha dataset, which contains high quality annotations of the processconditions, which occur during the project life.

The model development strategy is to start with an initial “rough” model(the consequence of a questionable training data set) then use the modelto trigger the gathering of a high quality training data set. As themodel is used to monitor the process, annotations and data will begathered on normal operations, special operations, and abnormaloperations. Anytime the model flags an abnormal operation or an abnormalevent is missed by the model, the cause and duration of the event isannotated. In this way feedback on the model's ability to monitor theprocess operation can be incorporated in the training data. This data isthen used to improve the model, which is then used to continue to gatherbetter quality training data. This process is repeated until the modelis satisfactory.

IV. Data & Process Analysis

A. Initial Rough Data Analysis

Using the operating logs and examining the process key performanceindicators, the historical data is divided into periods with knownabnormal operations and periods with no identified abnormal operations.The data with no identified abnormal operations will be the trainingdata set.

Now each measurement needs to be examined over its history to seewhether it is a candidate for the training data set. Measurements whichshould be excluded are:

-   -   Those with many long periods of time as “Bad Value”    -   Those with many long periods of time pegged to their transmitter        high or low limits    -   Those, which show very little variability (except those, which        are tightly controlled to their setpoints)    -   Those that continuously show very large variability relative to        their operating range    -   Those that show little or no cross correlation with any other        measurements in the data set    -   Those with poor signal to noise ratios

While examining the data, those time periods where measurements arebriefly indicating “Bad Value” or are briefly pegged to theirtransmitter high or low limits should also be excluded.

Once these exclusions have been made the first rough PCA model should bebuilt. Since this is going to be a very rough model the exact number ofprincipal components to be retained is not important. This willtypically be around 5% of the number measurements included in the model.The number of PCs should ultimately match the number of degrees offreedom in the process, however this is not usually known since thisincludes all the different sources of process disturbances. There areseveral standard methods for determining how many principal componentsto include. Also at this stage the statistical approach to variablescaling should be used: scale all variables to unit variance.X′=(X−X _(avg))/σ  Equation 7

The training data set should now be run through this preliminary modelto identify time periods where the data does not match the model. Thesetime periods should be examined to see whether an abnormal event wasoccurring at the time. If this is judged to be the case, then these timeperiods should also be flagged as times with known abnormal eventsoccurring. These time periods should be excluded from the training dataset and the model rebuilt with the modified data.

B. Removing Outliers and Periods of Abnormal Operations

Eliminating obvious abnormal events will be done through the following:

Removing documented events. It is very rare to have a complete record ofthe abnormal event history at a site. However, significant operatingproblems should be documented in operating records such as operatorlogs, operator change journals, alarm journals, and instrumentmaintenance records. These are only providing a partial record of theabnormal event history.

Removing time periods where key performance indicators, KPIs, areabnormal. Such measurements as feed rates, product rates, productquality are common key performance indicators. Each process operationmay have additional KPIs that are specific to the unit. Carefulexamination of this limited set of measurements will usually give aclear indication of periods of abnormal operations. FIG. 9 shows ahistogram of a KPI. Since the operating goal for this KPI is to maximizeit, the operating periods where this KPI is low are likely abnormaloperations. Process qualities are often the easiest KPIs to analyzesince the optimum operation is against a specification limit and theyare less sensitive to normal feed rate variations.

C. Compensating for Noise

By noise we are referring to the high frequency content of themeasurement signal which does not contain useful information about theprocess. Noise can be caused by specific process conditions such astwo-phase flow across an orifice plate or turbulence in the level. Noisecan be caused by electrical inductance. However, significant processvariability, perhaps caused by process disturbances is usefulinformation and should not be filtered out.

There are two primary noise types encountered in refining and chemicalprocess measurements: measurement spikes and exponentially correlatedcontinuous noise. With measurement spikes, the signal jumps by anunreasonably large amount for a short number of samples before returningto a value near its previous value. Noise spikes are removed using atraditional spike rejection filter such as the Union filter.

The amount of noise in the signal can be quantified by a measure knownas the signal to noise ratio (see FIG. 10). This is defined as the ratioof the amount of signal variability due to process variation to theamount of signal variability due to high frequency noise. A value belowfour is a typical value for indicating that the signal has substantialnoise, and can harm the model's effectiveness.

Whenever the developer encounters a signal with significant noise, heneeds to make one of three choices. In order of preference, these are:

-   -   Fix the signal by removing the source of the noise (the best        answer)    -   Remove/minimize the noise through filtering techniques    -   Exclude the signal from the model

Typically for signals with signal to noise ratios between 2 and 4, theexponentially correlated continuous noise can be removed with atraditional low pass filter such as an exponential filter. The equationsfor the exponential filter are:Y ^(n) =P*Y ^(n−1)+(1−P)*X ^(n) Exponential filter equation  Equation 8P=Exp(−T _(s) /T _(f)) Filter constant calculation  Equation 9

-   -   Y^(n) is the current filtered value    -   Y^(n−1) is the previous filtered value    -   X^(n) is the current raw value    -   P is the exponential filter constant    -   T_(s) is the sample time of the measurement    -   T_(f) is the filter time constant

Signals with very poor signal to noise ratios (for example less than 2)may not be sufficiently improved by filtering techniques to be directlyincluded in the model. If the input is regarded as important, thescaling of the variable should be set to de-sensitize the model bysignificantly increasing the size of the scaling factor (typically by afactor in the range of 2-10).

D. Transformed Variables

Transformed variables should be included in the model for two differentreasons.

First, based on an engineering analysis of the specific equipment andprocess chemistry, known non-linearities in the process should betransformed and included in the model. Since one of the assumptions ofPCA is that the variables in the model are linearly correlated,significant process or equipment non-linearities will break down thiscorrelation structure and show up as a deviation from the model. Thiswill affect the usable range of the model.

Examples of well known non-linear transforms are:

-   -   Reflux to feed ratio in distillation columns    -   Log of composition in high purity distillation    -   Pressure compensated temperature measurement    -   Sidestream yield    -   Flow to valve position (FIG. 2)    -   Reaction rate to exponential temperature change

Second, the data from process problems, which have occurredhistorically, should also be examined to understand how these problemsshow up in the process measurements. For example, the relationshipbetween tower delta pressure and feedrate is relatively linear until theflooding point is reached, when the delta pressure will increaseexponentially. Since tower flooding is picked up by the break in thislinear correlation, both delta pressure and feed rate should beincluded. As another example, catalyst flow problems can often be seenin the delta pressures in the transfer line. So instead of including theabsolute pressure measurements in the model, the delta pressures shouldbe calculated and included.

E. Dynamic Transformations

FIG. 11 shows how the process dynamics can disrupt the correlationbetween the current values of two measurements. During the transitiontime one value is constantly changing while the other is not, so thereis no correlation between the current values during the transition.However these two measurements can be brought back into timesynchronization by transforming the leading variable using a dynamictransfer function. Usually a first order with deadtime dynamic model(shown in Equation 9 in the Laplace transform format) is sufficient totime synchronize the data. $\begin{matrix}{{Y^{\prime}(s)} = \frac{{\mathbb{e}}^{{- \Theta}\quad S}{Y(s)}}{{T\quad s} + 1}} & {{Equation}\quad 9}\end{matrix}$

-   -   Y—raw data    -   Y′—time synchronized data    -   T—time constant    -   Θ—deadtime    -   S—Laplace Transform parameter

This technique is only needed when there is a significant dynamicseparation between variables used in the model. Usually only 1-2% of thevariables requires this treatment. This will be true for thoseindependent variables such as setpoints which are often changed in largesteps by the operator and for the measurements which are significantlyupstream of the main process units being modeled.

F. Removing Average Operating Point

Continuous refining and chemical processes are constantly being movedfrom one operating point to another. These can be intentional, where theoperator or an optimization program makes changes to a key setpoints, orthey can be due to slow process changes such as heat exchanger foulingor catalyst deactivation. Consequently, the raw data is not stationary.These operating point changes need to be removed to create a stationarydataset. Otherwise these changes erroneously appear as abnormal events.

The process measurements are transformed to deviation variables:deviation from a moving average operating point. This transformation toremove the average operating point is required when creating PCA modelsfor abnormal event detection. This is done by subtracting theexponentially filtered value (see Equations 8 and 9 for exponentialfilter equations) of a measurement from its raw value and using thisdifference in the model.X′=X−X _(filtered)  Equation 10

-   -   X′—measurement transformed to remove operating point changes    -   X—original raw measurement    -   X_(filtered)—exponentially filtered raw measurement

The time constant for the exponential filter should be about the samesize as the major time constant of the process. Often a time constant ofaround 40 minutes will be adequate. The consequence of thistransformation is that the inputs to the PCA model are a measurement ofthe recent change of the process from the moving average operatingpoint.

In order to accurately perform this transform, the data should begathered at the sample frequency that matches the on-line system, oftenevery minute or faster. This will result in collecting 525,600 samplesfor each measurement to cover one year of operating data. Once thistransformation has been calculated, the dataset is resampled to get downto a more manageable number of samples, typically in the range of 30,000to 50,000 samples.

V. Model Creation

Once the specific measurements have been selected and the training dataset has been built, the model can be built quickly using standard tools.

A. Scaling Model Inputs

The performance of PCA models is dependent on the scaling of the inputs.The traditional approach to scaling is to divide each input by itsstandard deviation, σ, within the training data set.X _(i) ′=X _(i)/σ_(i)  Equation 11

For input sets that contain a large number of nearly identicalmeasurements (such as multiple temperature measurements of fixedcatalyst reactor beds) this approach is modified to further divide themeasurement by the square root of the number of nearly identicalmeasurements.

-   -   For redundant data groups        X _(i) ′=x _(i)/(σ_(i) *sqrt(N)  Equation 12    -   Where N=number of inputs in redundant data group

These traditional approaches can be inappropriate for measurements fromcontinuous refining and chemical processes. Because the process isusually well controlled at specified operating points, the datadistribution is a combination of data from steady state operations anddata from “disturbed” and operating point change operations. These datawill have overly small standard deviations from the preponderance ofsteady state operation data. The resulting PCA model will be excessivelysensitive to small to moderate deviations in the process measurements.

For continuous refining and chemical processes, the scaling should bebased on the degree of variability that occurs during normal processdisturbances or during operating point changes not on the degree ofvariability that occurs during continuous steady state operations. Fornormally unconstrained variables, there are two different ways ofdetermining the scaling factor.

First is to identify time periods where the process was not running atsteady state, but was also not experiencing a significant abnormalevent. A limited number of measurements act as the key indicators ofsteady state operations. These are typically the process key performanceindicators and usually include the process feed rate, the productproduction rates and the product quality. These key measures are used tosegment the operations into periods of normal steady state operations,normally disturbed operations, and abnormal operations. The standarddeviation from the time periods of normally disturbed operationsprovides a good scaling factor for most of the measurements.

An alternative approach to explicitly calculating the scaling based ondisturbed operations is to use the entire training data set as follows.The scaling factor can be approximated by looking at the datadistribuion outside of 3 standard deviations from the mean. For example,99.7% of the data should lie, within 3 standard deviations of the meanand that 99.99% of the data should lie, within 4 standard deviations ofthe mean. The span of data values between 99.7% and 99.99% from the meancan act as an approximation for the standard deviation of the“disturbed” data in the data set. See FIG. 12.

Finally, if a measurement is often constrained (see the discussion onsaturated variables) only those time periods where the variable isunconstrained should be used for calculating the standard deviation usedas the scaling factor.

B. Selecting the Number of Principal Components

PCA transforms the actual process variables into a set of independentvariables called Principal Components, PC, which are linear combinationsof the original variables (Equation 13).PC _(i) =A _(i,1) *X ₁ +A _(i,2) *X ₂ +A _(i,3) *X ₃+ . . .   Equation13

The process will have a number of degrees of freedom, which representthe specific independent effects that influence the process. Thesedifferent independent effects show up in the process data as processvariation. Process variation can be due to intentional changes, such asfeed rate changes, or unintentional disturbances, such as ambienttemperature variation.

Each principal component models a part of the process variability causedby these different independent influences on the process. The principalcomponents are extracted in the direction of decreasing variation in thedata set, with each subsequent principal component modeling less andless of the process variability. Significant principal componentsrepresent a significant source of process variation, for example thefirst principal component usually represents the effect of feed ratechanges since this is usually the source of the largest process changes.At some point, the developer must decide when the process variationmodeled by the principal components no longer represents an independentsource of process variation.

The engineering approach to selecting the correct number of principalcomponents is to stop when the groups of variables, which are theprimary contributors to the principal component no longer makeengineering sense. The primary cause of the process variation modeled bya PC is identified by looking at the coefficients, A_(i,n), of theoriginal variables (which are called loadings). Those coefficients,which are relatively large in magnitude, are the major contributors to aparticular PC. Someone with a good understanding of the process shouldbe able to look at the group of variables, which are the majorcontributors to a PC and assign a name (e.g. feed rate effect) to thatPC. As more and more PCs are extracted from the data, the coefficientsbecome more equal in size. At this point the variation being modeled bya particular PC is primarily noise.

The traditional statistical method for determining when the PC is justmodeling noise is to identify when the process variation being modeledwith each new PC becomes constant. This is measured by the PRESSstatistic, which plots the amount of variation modeled by eachsuccessive PC (FIG. 13). Unfortunately this test is often ambiguous forPCA models developed on refining and chemical processes.

VI. Model Testing & Tuning

The process data will not have a gaussian or normal distribution.Consequently, the standard statistical method of setting the trigger fordetecting an abnormal event at 3 standard deviations of the errorresidual should not be used. Instead the trigger point needs to be setempirically based on experience with using the model.

Initially the trigger level should be set so that abnormal events wouldbe signaled at a rate acceptable to the site engineer, typically 5 or 6times each day. This can be determined by looking at the SPE_(x)statistic for the training data set (this is also referred to as the Qstatistic or the DMOD_(x) statistic). This level is set so that realabnormal events will not get missed but false alarms will not overwhelmthe site engineer.

A. Enhancing the Model

Once the initial model has been created, it needs to be enhanced bycreating a new training data set. This is done by using the model tomonitor the process. Once the model indicates a potential abnormalsituation, the engineer should investigate and classify the processsituation. The engineer will find three different situations, eithersome special process operation is occurring, an actual abnormalsituation is occurring, or the process is normal and it is a falseindication.

The new training data set is made up of data from special operations andnormal operations. The same analyses as were done to create the initialmodel need to be performed on the data, and the model re-calculated.With this new model the trigger lever will still be set empirically, butnow with better annotated data, this trigger point can be tuned so as toonly give an indication when a true abnormal event has occurred.

Simple Engineering Models for Abnormal Event Detection

The physics, chemistry, and mechanical design of the process equipmentas well as the insertion of multiple similar measurements creates asubstantial amount of redundancy in the data from continuous refiningand chemical processes. This redundancy is called physical redundancywhen identical measurements are present, and calculational redundancywhen the physical, chemical, or mechanical relationships are used toperform independent but equivalent estimates of a process condition.This class of model is called an engineering redundancy model.

I. Two Dimensional Engineering Redundancy Models

This is the simplest form of the model and it has the generic form:F(y _(i))=G(x _(i))+filtered bias_(i)+operator bias+error_(i)  Equation14raw bias_(i) =F(y _(i))−{G(x _(i))+filtered bias_(i)+operatorbias}=error_(i)  Equation 15filtered bias_(i)=filtered bias i−1 +N*raw bias_(i−1)  Equation 16

-   -   N—convergence factor (e.g. 0.0001)    -   Normal operating range: xmin<x<xmax    -   Normal model deviation: −(max_error)<error<(max_error)

The “operator bias” term is updated whenever the operator determinesthat there has been some field event (e.g. opening a bypass flow) whichrequires the model to be shifted. On the operator's command, theoperator bias term is updated so that Equation 14 is exactly satisfied(error_(i)=0)

The “filtered bias” term updates continuously to account for persistentunmeasured process changes that bias the engineering redundancy model.The convergence factor, “N”, is set to eliminate any persistent changeafter a user specified time period, usually on the time scale of days.

The “normal operating range” and the “normal model deviation” aredetermined from the historical data for the engineering redundancymodel. In most cases the max_error value is a single value, however thiscan also be a vector of values that is dependent on the x axis location.

Any two dimensional equation can be represented in this manner. Materialbalances, energy balances, estimated analyzer readings versus actualanalyzer readings, compressor curves, etc. FIG. 14 shows a twodimensional energy balance.

As a case in point the flow versus valve position model is explained ingreater detail.

A. The Flow Versus Valve Position Model

A particularly valuable engineering redundancy model is the flow versusvalve position model. This model is graphically shown in FIG. 2. Theparticular form of this model is: $\begin{matrix}{{\frac{Flow}{\left( {{Delta\_ Pressure}/{Delta\_ Pressure}_{reference}} \right)^{a}} + {{filtered}\quad{bias}} + {{operator}\quad{bias}}} = {{Cv}\quad({VP})}} & {{Equation}\quad 17}\end{matrix}$where:

-   -   Flow: measured flow through a control valve    -   Delta_Pressure=closest measured upstream pressure closest        measured downstream pressure    -   Delta_Pressure_(reference): average Delta_Pressure during normal        operation    -   a: model parameter fitted to historical data    -   Cv: valve characteristic curve determined empirically from        historical data    -   VP: signal to the control valve (not the actual control valve        position)        The objectives of this model are to:    -   Detecting sticking/stuck control valves    -   Detecting frozen/failed flow measurements    -   Detecting control valve operation where the control system loses        control of the flow

This particular arrangement of the flow versus valve equation is chosenfor human factors reasons: the x-y plot of the equation in this form isthe one most easily understood by the operators. It is important for anyof these models that they be arranged in the way which is most likely tobe easily understood by the operators.

B. Developing the Flow Versus Valve Position Model

Because of the long periods of steady state operation experienced bycontinuous refining and chemical processes, a long historical record (1to 2 years) may be required to get sufficient data to span the operationof the control valve. FIG. 15 shows a typical stretch of Flow, ValvePosition, and Delta Pressure data with the long periods of constantoperation. The first step is to isolate the brief time periods wherethere is some significant variation in the operation, as shown. Thisshould be then mixed with periods of normal operation taken from variousperiods in history.

Often, either the Upstream_Pressure (often a pump discharge) or theDownstream_Pressure is not available. In those cases the missingmeasurement becomes a fixed model parameter in the model. If bothpressures are missing then it is impossible to include the pressureeffect in the model.

The valve characteristic curve can be either fit with a linear valvecurve, with a quadratic valve curve or with a piecewise linear function.The piecewise linear function is the most flexible and will fit any formof valve characteristic curve.

The theoretical value for “a” is ½ if the measurements are takendirectly across the valve. Rarely are the measurements positioned there.“a” becomes an empirically determined parameter to account for theactual positioning of the pressure measurements.

Often there will be very few periods of time with variations in theDelta_Pressure. The noise in the Delta_Pressure during the normalperiods of operation can confuse the model-fitting program. To overcomethis, the model is developed in two phases, first where a small dataset,which only contains periods of Delta_Pressure variation is used to fitthe model. Then the pressure dependent parameters (“a” and perhaps themissing upstream or downstream pressure) are fixed at the valuesdetermined, and the model is re-developed with the larger dataset.

C. Fuzzy-Net Processing of Flow versus Valve Abnormality Indications

As with any two-dimensional engineering redundancy model, there are twomeasures of abnormality, the “normal operating range” and the “normalmodel deviation”. The “normal model deviation” is based on a normalizedindex: the error/max_error. This is fed into a type 4 fuzzydiscriminator (FIG. 16). The developer can pick the transition fromnormal (value of zero) to abnormal (value of 1) in a standard way byusing the normalized index.

The “normal operating range” index is the valve position distance fromthe normal region. It typically represents the operating region of thevalve where a change in valve position will result in little or nochange in the flow through the valve. Once again the developer can usethe type 4 fuzzy discriminator to cover both the upper and lower ends ofthe normal operating range and the transition from normal to abnormaloperation.

D. Grouping Multiple Flow/Valve Models

A common way of grouping Flow/Valve models which is favored by theoperators is to put all of these models into a single fuzzy network sothat the trend indicator will tell them that all of their critical flowcontrollers are working. In that case, the model indications into thefuzzy network (FIG. 4) will contain the “normal operating range” and the“normal model deviation” indication for each of the flow/valve models.The trend will contain the discriminator result from the worst modelindication.

When a common equipment type is grouped together, another operatorfavored way to look at this group is through a Pareto chart of theflow/valves (FIG. 17). In this chart, the top 10 abnormal valves aredynamically arranged from the most abnormal on the left to the leastabnormal on the right. Each Pareto bar also has a reference boxindicating the degree of variation of the model abnormality indicationthat is within normal. The chart in FIG. 17 shows that “Valve 10” issubstantially outside the normal box but that the others are allbehaving normally. The operator would next investigate a plot for “Valve10” similar to FIG. 2 to diagnose the problem with the flow controlloop.

II. Multidimensional Engineering Redundancy Models

Once the dimensionality gets larger than 2, a single “PCA like” model isdeveloped to handle a high dimension engineering redundancy check.Examples of multidimensional redundancy are:

-   -   pressure 1=pressure 2= . . . . =pressure n    -   material flow into process unit 1=material flow out of process        unit 1= . . . =material flow into process unit 2

Because of measurement calibration errors, these equations will eachrequire coefficients to compensate. Consequently, the model set thatmust be first developed is:F ₁(y _(i))=a ₁ G ₁(x _(i))+filtered bias_(1,i)+operatorbias₁+error_(1,i)F ₂(y _(i))=a _(n) G ₂(x _(i))+filtered bias_(2,i)+operatorbias₂+error_(2,i)F _(n)(y _(i))=a _(n) G _(n)(x _(i))+filtered bias_(n,i)+operatorbias_(n)+error_(n,i)  Equation 18

These models are developed in the identical manner that the twodimensional engineering redundancy models were developed.

This set of multidimensional checks are now converted into “PCA like”models. This conversion relies on the interpretation of a principlecomponent in a PCA model as a model of an independent effect on theprocess where the principle component coefficients (loadings) representthe proportional change in the measurements due to this independenteffect. In FIG. 3, there are three independent and redundant measures,X1, X2, and X3. Whenever X3 changes by one, X1 changes by a₁ and X2changes by a₂. This set of relationships is expressed as a singleprinciple component model, P, with coefficients in unscaled engineeringunits as:P=a ₁ X1+a ₂ X2+a ₃ X3  Equation 19

-   -   Where a₃=1

This engineering unit version of the model can be converted to astandard PCA model format as follows:

Drawing analogies to standard statistical concepts, the conversionfactors for each dimension, X, can be based on the normal operatingrange. For example, using 3σ around the mean to define the normaloperating range, the scaled variables are defined as:X _(scale) =X _(normal operating range)/6σ  Equation 20

-   -   (99.7% of normal operating data should fall within 3σ of the        mean)        X_(mid)=X_(mid point of operating range)  Equation 21    -   (explicitly defining the “mean” as the mid point of the normal        operating range)        X′=(X−X _(mid))/X _(scale)  Equation 22    -   (standard PCA scaling once mean and σ are determined)        Then the P′ loadings for X_(i) are: $\begin{matrix}        {b_{i} = {\left( {a_{i}/X_{i - {scale}}} \right)/\left( {\sum\limits_{k = 1}^{N}\left( {a_{k}/X_{k - {scale}}} \right)^{2}} \right)^{1/2}}} & {{Equation}\quad 23}        \end{matrix}$    -   (the requirement that the loading vector be normalized)        This transforms P to        P′=b ₁ *X1+b ₂ *X2+ . . . +b _(n) *XN  Equation 24        P′ “standard deviation”=b ₁ +b ₂ + . . . +b _(n)  Equation 25

With this conversion, the multidimensional engineering redundancy modelcan now be handled using the standard PCA structure for calculation,exception handling, operator display and interaction.

Deploying PCA Models and Simple Engineering Models for Abnormal EventDetection

I. Operator and Known Event Suppression

Suppression logic is required for the following:

-   -   Provide a way to eliminate false indications from measurable        unusual events    -   Provide a way to clear abnormal indications that the operator        has investigated    -   Provide a way to temporarily disable models or measurements for        maintenance    -   Provide a way to disable bad acting models until they can be        retuned    -   Provide a way to permanently disable bad acting instruments.

There are two types of suppression. Suppression which is automaticallytriggered by an external, measurable event and suppression which isinitiated by the operator. The logic behind these two types ofsuppression is shown in FIGS. 18 and 19. Although these diagrams showthe suppression occurring on a fuzzified model index, suppression canoccur on a particular measurement, on a particular model index, on anentire model, or on a combination of models within the process area.

For operator initiated suppression, there are two timers, whichdetermine when the suppression is over. One timer verifies that thesuppressed information has returned to and remains in the normal state.Typical values for this timer are from 15-30 minutes. The second timerwill reactivate the abnormal event check, regardless of whether it hasreturned to the normal state. Typical values for this timer are eitherequivalent to the length of the operator's work shift (8 to 12 hours) ora very large time for semi-permanent suppression.

For event based suppression, a measurable trigger is required. This canbe an operator setpoint change, a sudden measurement change, or adigital signal. This signal is converted into a timing signal, shown inFIG. 20. This timing signal is created from the trigger signal using thefollowing equations:Y _(n) =P*Y _(n−1)+(1−P)*X _(n) Exponential filter equation  Equation 26P=Exp(−T _(s) /T _(f)) Filter constant calculation  Equation 27Z _(n) =X _(n) −Y _(n) Timing signal calculation  Equation 28

-   -   where:        -   Y_(n) the current filtered value of the trigger signal        -   Y_(n−1) the previous filtered value of the trigger signal        -   X_(n) the current value of the trigger signal        -   Z_(n) the timing signal shown in FIG. 20        -   P the exponential filter constant        -   T_(s) the sample time of the measurement        -   T_(f) the filter time constant

As long as the timing signal is above a threshold (shown as 0.05 in FIG.20), the event remains suppressed. The developer sets the length of thesuppression by changing the filter time constant, T_(f). Although asimple timer could also be used for this function, this timing signalwill account for trigger signals of different sizes, creating longersuppressions for large changes and shorter suppressions for smallerchanges.

FIG. 21 shows the event suppression and the operator suppressiondisabling predefined sets of inputs in the PCA model. The set of inputsto be automatically suppressed is determined from the on-line modelperformance. Whenever the PCA model gives an indication that theoperator does not want to see, this indication can be traced to a smallnumber of individual contributions to the Sum of Error Square index. Tosuppress these individual contributions, the calculation of this indexis modified as follows: $\begin{matrix}{E^{2} = {\sum\limits_{i = 1}^{n}{w_{i}e_{i}^{2}}}} & {{Equation}\quad 29}\end{matrix}$

-   -   w_(i)—the contribution weight for input i (normally equal to 1)    -   e_(i)—the contribution to the sum of error squared from input i

When a trigger event occurs, the contribution weights are set to zerofor each of the inputs that are to be suppressed. When these inputs areto be reactivated, the contribution weight is gradually returned to avalue of 1.

II. PCA Model Decomposition

Although the PCA model is built using a broad process equipment scope,the model indices can be segregated into groupings that better match theoperators' view of the process and can improve the sensitivity of theindex to an abnormal event.

Referring again to Equation 29, we can create several Sum of ErrorSquare groupings: $\begin{matrix}\begin{matrix}{E_{1}^{2} = {\sum\limits_{i = 1}^{l}{w_{i}e_{i}^{2}}}} \\{E_{2}^{2} = {\sum\limits_{i = l}^{k}{w_{i}e_{i}^{2}}}} \\\vdots \\{E_{m}^{2} = {\sum\limits_{i = k}^{n}{w_{i}e_{i}^{2}}}}\end{matrix} & {{Equation}\quad 30}\end{matrix}$

Usually these groupings are based around smaller sub-units of equipment(e.g. reboiler section of a tower), or are sub-groupings, which arerelevant to the function of the equipment (e.g. product quality).

Since each contributor, e_(i), is always adding to the sum of errorsquare based on process noise, the size of the index due to noiseincreases linearly with the number of inputs contributing to the index.With fewer contributors to the sum of error square calculation, thesignal to noise ratio for the index is improved, making the index moreresponsive to abnormal events.

In a similar manner, each principle component can be subdivided to matchthe equipment groupings and an index analogous to the Hotelling T² indexcan be created for each subgroup. $\begin{matrix}\begin{matrix}{P_{1,a} = {\sum\limits_{i = 1}^{l}{b_{1,i}x_{i}}}} \\{P_{1,b} = {\sum\limits_{i = l}^{k}{b_{1,i}x_{i}}}} \\{P_{1,c} = {\sum\limits_{i = k}^{n}{b_{1,i}x_{i}}}} \\{P_{2,a} = {\sum\limits_{i = 1}^{l}{b_{2,i}x_{i}}}} \\{P_{2,b} = {\sum\limits_{i = l}^{k}{b_{2,i}x_{i}}}} \\{P_{2,c} = {\sum\limits_{i = k}^{n}{b_{2,i}x_{i}}}} \\{T_{a}^{2} = {\sum\limits_{i = 1}^{m}P_{i,a}^{2}}} \\{T_{b}^{2} = {\sum\limits_{i = 1}^{m}P_{i,b}^{2}}} \\{T_{c}^{2} = {\sum\limits_{i = 1}^{m}P_{i,c}^{2}}}\end{matrix} & {{Equation}\quad 31}\end{matrix}$

The thresholds for these indices are calculated by running the testingdata through the models and setting the sensitivity of the thresholdsbased on their performance on the test data.

These new indices are interpreted for the operator in the identicalmanner that a normal PCA model is handled. Pareto charts based on theoriginal inputs are shown for the largest contributors to the sum oferror square index, and the largest contributors to the largest P in theT² calculation.

III. Overlapping PCA Models

Inputs will appear in several PCA models so that all interactionsaffecting the model are encompassed within the model. This can causemultiple indications to the operator when these inputs are the majorcontributors to the sum of error squared index.

To avoid this issue, any input, which appears in multiple PCA models, isassigned one of those PCA models as its primary model. The contributionweight in Equation 29 for the primary PCA model will remain at one whilefor the non-primary PCA models, it is set to zero.

IV. Operator Interaction & Interface Design

The primary objectives of the operator interface are to:

-   -   Provide a continuous indication of the normality of the major        process areas under the authority of the operator    -   Provide rapid (1 or 2 mouse clicks) navigation to the underlying        model information    -   Provide the operator with control over which models are enabled.        FIG. 22 shows how these design objectives are expressed in the        primary interfaces used by the operator.

The final output from a fuzzy Petri net is a normality trend as is shownin FIG. 4. This trend represents the model index that indicates thegreatest likelihood of abnormality as defined in the fuzzy discriminatefunction. The number of trends shown in the summary is flexible anddecided in discussions with the operators. On this trend are tworeference lines for the operator to help signal when they should takeaction, a yellow line typically set at a value of 0.6 and a red linetypically set at a value of 0.9. These lines provide guidance to theoperator as to when he is expected to take action. When the trendcrosses the yellow line, the green triangle in FIG. 4 will turn yellowand when the trend crosses the red line, the green triangle will turnred. The triangle also has the function that it will take the operatorto the display associated with the model giving the most abnormalindication.

If the model is a PCA model or it is part of an equipment group (e.g.all control valves), selecting the green triangle will create a Paretochart. For a PCA model, of the dozen largest contributors to the modelindex, this will indicate the most abnormal (on the left) to the leastabnormal (on the right) Usually the key abnormal event indicators willbe among the first 2 or 3 measurements. The Pareto chart includes a redbox around each bar to provide the operator with a reference as to howunusual the measurement can be before it is regarded as an indication ofabnormality.

For PCA models, operators are provided with a trend Pareto, whichmatches the order in the bar chart Pareto. With the trend Pareto, eachplot has two trends, the actual measurement (in cyan) and an estimatefrom the PCA model of what that measurements should have been ifeverything was normal (in tan).

For valve/flow models, the detail under the Pareto will be the twodimensional flow versus valve position model plot. From this plot theoperator can apply the operator bias to the model.

If there is no equipment grouping, selecting the green triangle willtake the operator right to the worst two-dimensional model under thesummary trend.

Operator suppression is done at the Pareto chart level by selecting theon/off button beneath each bar.

BIBLIOGRAPHY

U.S. Patent Documents

-   1 U.S. Pat. No. 5,859,964 Jan. 12, 1999 Wang, et al, “System and    method for performing real time data acquisition, process modeling    and fault detection of wafer fabrication processes”-   2 U.S. Pat. No. 5,949,678 Sep. 7, 1999 Wold, et al, “Method for    Monitoring Multivariable Processes”-   3 U.S. Pat. No. 6,522,978 Feb. 18, 2002 Chen, et al, “Paper web    breakage prediction using principal components analysis and    classification and regression trees”-   4 U.S. Pat. No. 6,368,975 Apr. 9, 2002 Balasubramhanya, et al,    “Method and apparatus for monitoring a process by employing    principal component analysis”-   5 U.S. Pat. No. 6,466,877 Oct. 15, 2002 Chen, et al, “Paper web    breakage prediction using principal components analysis and    classification and regression trees”-   6 U.S. Pat. No. 6,521,080 Feb. 18, 2003 Balasubramhanya, et al,    “Method and apparatus for monitoring a process by employing    principal component analysis”-   7 U.S. Pat. No. 6,564,119 May 13, 2003 Vaculik, et al, “Multivariate    Statistical Model Based System for Monitoring the Operation of a    Continuous Caster and Detecting the Onset of Impending Breakouts”-   8 U.S. Pat. No. 6,636,842 Oct. 21, 2003 Zambrano, et al, “System and    method for controlling an industrial process utilizing process    trajectories”    II. Literature-   1. Cardoso, J. et al “Fuzzy Petri Nets: An Overview”, 13^(th) Word    Congress of IFAC, Vol. I: Identification II, Discrete Event Systems,    San Francisco, Calif., USA, Jun. 30-Jul. 5, 1996, pp. 443-448.-   2. Jackson, E. “A User's Guide to Principal Component Analysis”,    John Wiley & Sons, 1991-   3. Kourti, T. “Process Analysis and Abnormal Situation Detection:    From Theory to Practice”, IEEE Control Systems Magazine, October    2002, pp. 10-25-   4. Ku, W. “Disturbance Detection and Isolation for Statistical    Process Control in Chemical Processes”, PhD Thesis, Lehigh    University, Aug. 17, 1994-   5. Martens, H., & Naes, T., “Multivariate Calibration”, John Wiley &    Sons, 1989-   6. Piovoso, M. J., et al. “Process Data Chemometrics”, IEEE Trans on    Instrumentation and Measurement, Vol. 41, No. 2, April 1992, pp.    262-268

Appendix 2

TABLE 1 R1R2 Principal Components With Sensor Title and PrincipalComponent Loading Sensor Description Loading 1. Stage 1 Hydrogen HeaderPressure 1ST STAGE RECYCLE COMPRESSOR DISCHARGE PRESSURE 0.180 ALT 1STSTAGE RECYCLE COMPRESSOR DISCHARGE PRESSURE 0.178 R2 REACTOR EFFLUENTTEMPERATURE −0.176 ALT 1ST STAGE RECYCLE COMPRESSOR SUCTION PRESSURE0.174 1ST STAGE RECYCLE COMPRESSOR SUCTION PRESSURE 0.173 1ST STAGE HIGHPRESSURE SEPARATOR PRESSURE 0.169 R1 QUENCH SUPPLY/REACTOR PRESSUREDIFFERENTIAL 0.169 R1 HYDROGEN PREHEAT EXCHANGER EFFLUENT TEMPERATURE−0.168 HYDROGEN RECYCLE TO R1 INLET FLOW 0.167 R2 QUENCH SUPPLY/REACTORPRESSURE DIFFERENTIAL 0.165 2. R2 Reactor Hydrofining Conversion R2REACTOR DIFFERENTIAL TEMPERATURE −0.180 PRESSURE CORRECTED R1 BED 3QUENCH VALVE POSITION 0.171 R1 BED 3 QUENCH FLOW 0.161 R1 BED 2/BED 3TEMPERATURE DIFFERENTIAL 0.152 R1 BED 4 BOTTOM TEMPERATURE AVERAGE−0.152 R1 BED 5 BOTTOM TEMPERATURE AVERAGE −0.151 TOTAL QUENCH HYDROGENFLOW TO R1 0.146 TOTAL RECYCLE HYDROGEN FLOW TO R1/R2 0.142 R1 EAST FEEDPREHEATER OUTLET TEMPERATURE 0.140 R1 BED 1 TOP TEMPERATURE AVERAGE0.138 3. Stage 1 Offgas Hydrogen 400# STEAM FLOW TO 1ST STAGE RECYCLECOMPRESSOR 0.178 COMBINED LOW PRESSURE SEPARATOR OFFGAS PRESSURE 0.176COMBINED LOW PRESSURE SEPARATOR OFFGAS FLOW 0.175 PRESSURE CORRECTED R2BED 5 QUENCH VALVE POSITION 0.173 1ST STAGE LOW PRESSURE SEPARATORBOTTOMS TEMPERATURE 0.171 PRESSURE CORRECTED R2 BED 4 QUENCH VALVEPOSITION 0.169 TOTAL HYDROGEN MAKE UP TO R1/R2 0.165 1ST STAGE HIGHPRESSURE SEPARATOR INLET TEMPERATURE 0.164 R1 INLET PRESSURE CONTROLVALVE POSITION 0.164 1ST STAGE RECYCLE COMPRESSOR SUCTION TEMPERATURE0.161 4. Stage 1 PreHeat R1 WEST FEED PREHEATER OUTLET TEMPERATURE−0.200 ALT R1 WEST FEED PREHEATER OUTLET TEMPERATURE −0.198 R1 INLETTEMPERATURE −0.185 R1 EAST FEED PREHEATER OUTLET TEMPERATURE −0.170 R2INLET TEMPERATURE −0.169 R1 EAST FEED PREHEATER OUTLET TEMPERATURE−0.162 R1 EAST FEED PREHEATER FUEL GAS FLOW −0.159 R1 EAST FEEDPREHEATER FUEL GAS PRESSURE −0.158 R1 WEST FEED PREHEATER STACKTEMPERATURE −0.158 R1 EAST FEED PREHEATER FUEL GAS PRESSURE OUTPUT−0.155 5. R1 Reactor Quench PRESSURE COMPENSATED R1 BED 4 QUENCH VALVEPOSITION 0.222 R1 BED 4 QUENCH FLOW 0.216 R1 BED 3 BOTTOM TEMPERATUREAVERAGE 0.183 R1 BED 1/BED 2 TEMPERATURE DIFFERENTIAL −0.167 R1 BED 5QUENCH FLOW 0.164 R1 BED 2 QUENCH FLOW −0.161 R1 BED 3 TOP TEMPERATUREAVERAGE 0.160 R1 BED 3/BED 4 TEMPERATURE DIFFERENTIAL 0.159 R1 BED 6BOTTOM TEMPERATURE AVERAGE −0.157 PRESSURE COMPENSATED R1 BED 5 QUENCHVALVE POSITION 0.155 6. Stage 1 Average Temperature R1 BED 4 TOPTEMPERATURE AVERAGE −0.211 R1 BED 4 BOTTOM TEMPERATURE AVERAGE −0.195PRESSURE COMPENSATED R1 BED 5 QUENCH VALVE POSITION −0.186 R2 BED 4TEMPERATURE DROP 0.182 R2 REACTOR EFFLUENT TEMPERATURE 0.176 R2 BED 4BOTTOM TEMPERATURE AVERAGE 0.166 R2 BED 3 BOTTOM TEMPERATURE AVERAGE0.158 R1 BED 4/BED 5 TEMPERATURE DIFFERENTIAL −0.156 R2 BED 3TEMPERATURE DROP 0.156 R2 BED 5 BOTTOM TEMPERATURE AVERAGE 0.153 7.Stage 1 Feed R1 TOTAL FEED FLOW −0.260 1ST STAGE CHARGE PUMP MIN FLOW−0.237 ALT 1ST STAGE CHARGE PUMP MIN FLOW −0.229 1ST STG CHARGE PUMPDISCHARGE PRESSURE 0.224 R1 WEST FEED PREHEATER FLOW −0.223 R1 EAST FEEDPREHEATER FLOW −0.216 R1 WEST FEED PREHEATER FLOW OUTPUT −0.206 R1 EASTFEED PREHEATER FLOW OUTPUT −0.176 R1 HIGH PRESSURE MAKE-UP HYDROGEN FLOW−0.173 RECIPROCATING COMPRESSOR HIGH PRESSURE DISCHARGE FLOW −0.169 8.Stage 1 Pressure Balance R2 REACTOR INLET PRESSURE 0.273 R1 REACTOREFFLUENT OUTLET PRESSURE 0.273 R1 INLET PRESSURE 0.270 R2 INLET PRESSURE0.267 R2 REACTOR OUTLET PRESSURE 0.253 1ST STAGE RECYCLE COMPRESSORSUCTION PRESSURE 0.167 ALT 1ST STAGE RECYCLE COMPRESSOR SUCTION PRESSURE0.166 1ST STAGE HIGH PRESSURE SEPARATOR PRESSURE 0.164 R1 BED 2 BOTTOMTEMPERATURE AVERAGE 0.143 R2 PRODUCT THRU STABILIZER EXCHANGERTEMPERATURE 0.138 9. Stage 1 Make-Up Hydrogen R1 HIGH PRESSURE MAKE-UPHYDROGEN FLOW −0.188 RECIPROCATING COMPRESSOR HIGH PRESSURE DISCHARGEFLOW −0.174 HIGH PRESSURE HYROGEN MAKE-UP R1 EAST PREHEATER FLOW −0.170R2 BED 4 BOTTOM TEMPERATURE AVERAGE −0.168 R2 RECYCLE HYDROGEN RATIO−0.164 HIGH PRESSURE HYDROGEN MAKE-UP R1 WEST PREHEATER FLOW −0.161 R1EAST FEED PREHEATER STACK TEMPERATURE −0.159 R1 EAST FEED PREHEATERFIREBOX TEMPERATURE −0.152 LP HYDROGEN TO HIGH PRESSURE KNOCKOUT DRUMINLET FLOW −0.147 R2 BED 5 QUENCH FLOW −0.141 10. Recycle Compressor ALT1ST STAGE RECYCLE COMPRESSOR SUCTION PRESSURE 0.198 1ST STAGE LOWPRESSURE SEPARATOR BOTTOMS TEMPERATURE 0.190 1ST STAGE HIGH PRESSURESEPARATOR INLET TEMPERATURE 0.179 400# STEAM FLOW TO 1ST STAGE RECYCLECOMPRESSOR −0.178 1ST STAGE RECYCLE COMPRESSOR SUCTION TEMPERATURE 0.1661ST STAGE RECYCLE COMPRESSOR DIFFERENTIAL PRESSURE −0.157 HIGH PRESSUREHYDROGEN TO R1/R2 TEMPERATURE 0.153 R2 RECYCLE HYDROGEN RATIO −0.145COMBINED LP SEPARATOR ABSORBER OFF GAS TEMPERATURE 0.139 R1 RECYCLEHYDROGEN RATIO −0.131 11. Make-Up Hydrogen/PreHeat R1 HIGH PRESSUREMAKE-UP HYDROGEN FLOW −0.224 RECIPROCATING COMPRESSOR HIGH PRESSUREDISCHARGE FLOW −0.218 HIGH PRESSURE HYDROGEN MAKE-UP R1 WEST PREHEATERFLOW −0.199 HIGH PRESSURE MAKE-UP HYDROGEN TO EAST PREHEATER FLOW −0.196LP HYDROGEN TO HIGH PRESSURE KNOCKOUT DRUM INLET FLOW −0.186 HIGHPRESSURE H2 KNOCKOUT DRUM INLET PRESSURE VALVE −0.173 RECIPROCATINGCOMPRESSOR LOW PRESSURE DISCHARGE −0.173 PRESSURE R1 EAST FEED PREHEATERINLET TEMPERATURE 0.167 R1 EAST FEED PREHEATER OUTLET TEMPERATURE 0.164ALT R1 WEST FEED PREHEATER OUTLET TEMPERATURE 0.155 12. ReciprocatingCompressor HIGH PRESSURE H2 KNOCKOUT DRUM INLET PRESSURE VALVE −0.219RECIP COMPRESSOR LOW PRESSURE DISCHARGE PRESSURE −0.219 LOW PRESSUREKNOCKOUT DRUM INLET PRESSURE 0.185 RECIP COMPRESSOR LOW PRESSURE SUCTIONPRESSURE 0.185 1ST STAGE RECYCLE COMPRESSOR DIFFERENTIAL TEMPERATURE−0.169 TOTAL HYDROGEN MAKE UP TO R1/R2 −0.158 R2 BED 1 QUENCH FLOW 0.157LP HYDROGEN TO HIGH PRESSURE KNOCKOUT DRUM INLET FLOW −0.154 R1 HIGHPRESSURE MAKE-UP HYDROGEN FLOW −0.145 1ST STAGE RECYCLE COMPRESSORSUCTION TEMPERATURE 0.145 13. Low Purity Separator 1ST STAGE LOWPRESSURE SEPARATOR BOTTOMS FLOW OUTPUT 0.190 1ST STAGE LOW PRESSURESEPARATOR PRESSURE −0.174 LOW PRESSURE SEPARATOR OFFGAS ABSORBER LEVELOUTPUT 0.166 LOW PRESSURE SEPARATOR OFFGAS ABSORBER BOTTOMS FLOW 0.1611ST STAGE LOW PRESSURE SEPARATOR OFFGAS PRESSURE −0.159 R1 BED 6 QUENCHFLOW 0.152 R1 BED 6 TOP AVERAGE TEMPERATURE −0.152 R1/R2 DIFFERENTIALTEMPERATURE 0.150 1ST STAGE LOW PRESSURE SEPARATOR BOTTOMS FLOW 0.149 R1BED 5/BED 6TEMPERATURE DIFFERENTIAL 0.148

TABLE 2 R3 Principal Components With Sensor Title and PrincipalComponent Loading Sensor Description Loading 1. Overall R3 TemperatureR3 AVERAGE REACTOR TEMPERATURE 2.86E−01 R3 TOTAL DIFFERENTIALTEMPERATURE 2.48E−01 R3 BED1 AVERAGE TEMPERATURE 2.08E−01 R3 BED4 DELTAT 2.08E−01 R3 BED1 DELTA T 2.02E−01 R3 FEED PREHEATER OUTLET TEMPERATURE1.87E−01 R3 REACTOR FEED TEMPERATURE 1.86E−01 R3 BED2 AVERAGETEMPERATURE 1.86E−01 R3 BED4 AVERAGE TEMPERATURE 1.84E−01 2ND STAGE HPSEPARATOR PRESSURE VALVE POSITION 1.78E−01 2. Non-R3 Temperatures 2NDSTAGE LOW PRESSURE SEPARATOR BOTTOMS TEMPERATURE 2.67E−01 2ND STAGERECYCLE COMPRESSOR DISCHARGE TEMPERATURE 2.66E−01 R3 EFFLUENTTEMPERATURE 2.57E−01 H2 TO R3 REACTOR TEMPERATURE 2.53E−01 R3 FEEDTEMPERATURE 2.45E−01 2ND STAGE HIGH PRESSURE SEPARATOR INLET TEMPERATURE2.45E−01 R3 SURGE DRUM OUTLET TEMPERATURE 2.44E−01 2ND STAGE LOWPRESSURE SEPARATOR BOTTOMS TEMPERATURE 2.39E−01 R3 BED 3 DIFFERENTIALPRESSURE 2.24E−01 R3 SURGE DRUM INLET TEMPERATURE 2.13E−01 3. HPSeparator Pressure 2ND STAGE HIGH PRESSURE SEPARATOR PRESSURE −2.95E−012ND STAGE RECYCLE COMPRESSOR DISCHARGE PRESSURE −2.90E−01 ALT 2ND STAGERECYCLE COMPRESSOR SUCTION PRESSURE −2.86E−01 2ND STAGE RECYCLECOMPRESSOR SUCTION PRESSURE −2.59E−01 R3 SURGE DRUM OUTLET TEMPERATURE−2.04E−01 R3 FEED TEMPERATURE −2.03E−01 R3 REACTOR FEED FLOW −1.91E−01R3 BED 5 AVERAGE TEMPERATURE −1.85E−01 2ND STAGE RECYCLE COMPRESSORSUCTION PRESSURE OUTPUT 1.83E−01 R3 PRODUCT DISCHARGE TEMPERATURE−1.78E−01 4. HP Separator Temperatures 2ND STAGE HIGH PRESSURE SEPARATORINLET TEMPERATURE −2.52E−01 H2 TO R3 REACTOR TEMPERATURE −2.50E−01 2NDSTAGE RECYCLE COMPRESSOR DISCHARGE TEMPERATURE −2.49E−01 R3 SURGE DRUMOUTLET TEMPERATURE 2.46E−01 R3 FEED TEMPERATURE 2.45E−01 R3 SURGE DRUMINLET TEMPERATURE 2.15E−01 2ND STAGE RECYCLE COMPRESSOR DISCHARGEPRESSURE −2.11E−01 R3 EFFLUENT TEMPERATURE 2.11E−01 2ND STAGE RECYCLECOMPRESSOR DISCHARGE TEMPERATURE −2.01E−01 ALT 2ND STAGE RECYCLECOMPRESSOR SUCTION PRESSURE −1.95E−01 5. Recycle Compressor 2ND STAGERECYCLE COMPRESSOR DISCHARGE TEMPERATURE −3.98−01 ALT 2ND STAGE RECYCLECOMPRESSOR SUCTION PRESSURE −3.90E−01 2ND STAGE RECYCLE COMPRESSORDIFFERENTIAL PRESSURE −3.19E−01 2ND STAGE LOW PRESSURE SEPARATOR OFFGASTEMPERATURE 3.09E−01 R3 BED 3 DIFFERENTIAL PRESSURE 2.52E−01 2ND STAGERECYCLE COMPRESSOR SPEED −1.85E−01 2ND STAGE RECYCLE GAS PURITY−1.64E−01 2ND STAGE LOW PRESSURE SEPARATOR BOTTOMS TEMPERATURE 1.55E−012ND STAGE HIGH PRESSURE SEPARATOR FAN OUTLET TEMPERATURE −1.51E−01 2NDSTAGE RECYCLE COMPRESSOR SUCTION PRESSURE OUTPUT −1.38E−01 6. R3 Bed 3Operation R3 BED 3 AVERAGE TEMPERATURE −3.34E−01 R3 BED 3 DIFFERENTIALTEMPERATURE −3.23E−01 R3 BED 3 INLET TEMPERATURE −2.72E−01 R3 BED1AVERAGE TEMPERATURE 2.58E−01 R3 BED 4 QUENCH FLOW OUTPUT −2.34E−01 R3REACTOR FEED TEMPERATURE 2.29E−01 R3 BED 4 QUENCH FLOW OUTPUT −2.27E−01R3 FEED PREHEATER OUTLET TEMPERATURE 2.23E−01 R3 BED 2 QUENCH FLOW2.23E−01 R3 BED 5 QUENCH FLOW −1.98E−01 7. R3 Temperature Profile R3 BED3 QUENCH FLOW 3.20E−01 R3 BED 2 DIFFERENTIAL TEMPERATURE 3.05E−01 R3 BED3 QUENCH FLOW OUTPUT 2.99E−01 R3 BED 4 INLET TEMPERATURE −2.74E−01 R3BED 5 INLET TEMPERATURE −2.67E−01 R3 BED 1 DIFFERENTIAL TEMPERATURE2.30E−01 R3 REACTOR FEED TEMPERATURE −2.16E−01 R3 FEED PREHEATER OUTLETTEMPERATURE −2.12E−01 R3 TOTAL DIFFERENTIAL TEMPERATURE 2.02E−01 2NDSTAGE HIGH PRESSURE SEPARATOR PRESSURE 1.86E−01 8. Reactor Pressure R3INLET PRESSURE 3.38E−01 R3 INLET PRESSURE OUTPUT −2.86E−01 R3 BED 5AVERAGE TEMPERATURE −2.53E−01 R3 PRODUCT DISCHARGE TEMPERATURE −2.37E−01R3 BED 3 INLET TEMPERATURE 2.19E−01 R3 FEED PREHEATER OUTLET TEMPERATURE2.08E−01 R3 BED 2 INLET TEMPERATURE 2.07E−01 2ND STAGE RECYCLECOMPRESSOR SUCTION PRESSURE OUTPUT −1.99E−01 R3 BED 5 DIFFERENTIALTEMPERATURE −1.95E−01 R3 BED 5 INLET TEMPERATURE −1.81E−01 9. Make-UpHydrogen LOW PRESSURE HYDROGEN MAKE-UP TO R3 FLOW −3.68E−01 LOW PRESSUREHYDROGEN DISCHARGE PRESSURE OUTPUT 3.47E−01 R3 INLET PRESSURE OUTPUT−3.35E−01 LOW PRESSURE HYDROGEN DISCHARGE PRESSURE 3.35E−01 R3 INLETPRESSURE 2.26E−01 R3 BED 5 AVERAGE TEMPERATURE 2.06E−01 R3 BED 5DIFFERENTIAL TEMPERATURE 1.94E−01 RECIPROCATING COMPRESSOR LP DISCHARGEPRESSURE 1.86E−01 2ND STAGE RECYCLE COMPRESSOR SUCTION PRESSURE OUTPUT−1.67E−01 RECYCLE HYDROGEN TO R3 FEED FLOW −1.63E−01 10. PressureProfile R3 BED 4 DIFFERENTIAL PRESSURE 3.51E−01 R3 BED2 AVERAGETEMPERATURE 3.21E−01 R3 BED 2 INLET TEMPERATURE 3.19E−01 R3 BED 5DIFFERENTIAL PRESSURE −3.00E−01 R3 BED 5 QUENCH FLOW −2.78E−01 R3 BED 2DIFFERENTIAL PRESSURE −2.56E−01 R3 BED 4 AVERAGE TEMPERATURE −2.37E−01R3 BED 4 DIFFERENTIAL TEMPERATURE −2.27E−01 R3 BED 2 QUENCH FLOW−2.11E−01 R3 BED 3 QUENCH FLOW 2.07E−01 11. Quench DP Profile R3 BED 4DIFFERENTIAL PRESSURE −3.86E−01 R3 BED 2 DIFFERENTIAL PRESSURE −3.47E−01R3 BED 4 INLET TEMPERATURE 3.24E−01 R3 BED 2 QUENCH FLOW −2.68E−01 R3BED 4 QUENCH FLOW OUTPUT −2.56E−01 R3 BED 4 AVERAGE TEMPERATURE 2.31E−01R3 BED 5 DIFFERENTIAL PRESSURE 2.23E−01 R3 BED 3 QUENCH FLOW 2.19E−01 R3BED2 AVERAGE TEMPERATURE 1.93E−01 R3 BED 5 QUENCH FLOW 1.89E−01 12.Make-up H2 Pressure LOW PRESSURE HYDROGEN DISCHARGE PRESSURE OUTPUT4.95E−01 LOW PRESSURE HYDROGEN DISCHARGE PRESSURE 4.37E−01 R3 INLETPRESSURE −3.02E−01 R3 INLET PRESSURE OUTPUT 2.88E−01 2ND STAGE RECYCLECOMPRESSOR SUCTION PRESSURE 2.22E−01 ALT 2ND STAGE RECYCLE COMPRESSORSUCTION PRESSURE 2.21E−01 RECYCLE HYDROGEN TO R3 FEED FLOW 2.09E−01 R3TOTAL DIFFERENTIAL PRESSURE −1.58E−01 1ST STAGE HIGH PRESSURE SEPARATOROVERHEAD FLOW −1.42E−01 LOW PRESSURE HYDROGEN MAKE-UP TO R3 TEMPERATURE−1.34E−01 13. Bed 2 Quench/Dp R3 BED 2 DIFFERENTIAL PRESSURE 4.27E−01 R3BED 2 QUENCH FLOW 3.56E−01 R3 BED 2 QUENCH FLOW OUTPUT −3.19E−01 R3 FEEDPREHEATER OUTLET TEMPERATURE VALVE POSITION −2.69E−01 RECYCLE HYDROGENTO R3 FEED FLOW −2.50E−01 R3 BED 5 DIFFERENTIAL TEMPERATURE −2.47E−01 R3BED 5 INLET TEMPERATURE 1.96E−01 1ST STAGE COMPRESSOR SPILLBACK FLOW1.83E−01 R3 BED 2 DIFFERENTIAL TEMPERATURE 1.81E−01 R3 BED 4 INLETTEMPERATURE 1.81E−01

TABLE 3 Fractionation Principal Components With Sensor Title andPrincipal Component Loading Sensor Description Loading 1. Heat Input toSplitter Tower STABILIZER TOWER BOTTOMS TEMPERATURE 2.44E−01 SPLITTERTOWER FEED ZONE TEMPERATURE 2.35E−01 PRESSURE COMPENSATED SPLITTER TOWERTRAY 10 TEMPERATURE 2.22E−01 SPLITTER TOWER LIQUID FEED TEMPERATURE2.19E−01 PRESSURE COMPENSATED STABILIZER TOWER TRAY 25 TEMPERATURE2.12E−01 PRESSURE COMPENSATED STABILIZER TOWER TRAY 33 TEMPERATURE2.12E−01 PRESSURE COMPENSATED SPLITTER TOWER TRAY 18 TEMPERATURE2.04E−01 PRESSURE COMPENSATED STABILIZER TOWER TRAY 15 TEMPERATURE2.03E−01 PRESSURE COMPENSATED SPLITTER TOWER TRAY 22 TEMPERATURE1.99E−01 PRESSURE COMPENSATED SPLITTER TOWER TRAY 2 TEMPERATURE 1.95E−012. Splitter Bottoms Draw Effect SPLITTER BOTTOMS FLOW TO FEED FLOW RATIO−2.40E−01 HEAVY NAPTHA STRIPPER BOTTOMS TEMPERATURE 2.30E−01 PRESSURECOMPENSATED SPLITTER TOWER TRAY 22 TEMPERATURE 2.30E−01 PRESSURECOMPENSATED SPLITTER TOWER TRAY 20 TEMPERATURE 2.29E−01 HOT HEAVY NAPTHATO REFORMER TEMPERATURE 2.09E−01 PRESSURE COMPENSATED SPLITTER TOWERTRAY 28 TEMPERATURE 2.06E−01 PRESSURE COMPENSATED SPLITTER TOWER TRAY 18TEMPERATURE 2.01E−01 STABILIZER BOTTOMS FLOW TO TOTAL PRODUCT FLOW RATIO1.96E−01 PRESSURE COMPENSATED SPLITTER TOWER TRAY 38 TEMPERATURE1.91E−01 SPLITTER OVERHEAD CONDENSOR TEMPERATURE 1.88E−01 3. AmbientTemperature on Condensation SPLITTER ACCUMULATOR INLET TEMPERATURE3.90E−01 SPLITTER OVERHEAD CONDENSOR INLET TEMPERATURE 3.76E−01 AMBIENTTEMPERATURE 3.47E−01 STABILIZER OVERHEAD ACCUMULATOR LIQUID TEMPERATURE3.44E−01 ALTERNATE AMBIENT AIR TEMPERATURE MEASUREMENT 3.38E−01STABILIZER TOWER OVERHEAD TEMPERATURE 1.98E−01 FIRST STAGE SURGE DRUMOUTLET TEMPERATURE 1.79E−01 SPLITTER REFLUX FLOW TO FEED FLOW RATIO1.79E−01 LOG STABILIZER PERCENT IC5 IN OVERHEAD ANALYZER 1.69E−01PRESSURE COMPENSATED STABILIZER TOWER TRAY 40 TEMPERATURE 1.45E−01 4.Stabilizer Material Allocation BUTANE DRAW RATE TO TOTAL STABILIZERPRODUCT FLOW RATIO 3.39E−01 STABILIZER BOTTOMS FLOW TO STABILIZERPRODUCT FLOW RATIO −2.91E−01 1ST STAGE LPS BOTTOMS FLOW TO TOTAL FEEDRATIO −2.74E−01 STABILIZER TOWER INLET TEMPERATURE −2.40E−01 STABILIZERDISTILLATE RATIO (REFLUX TO OVERHEAD PROD FLOW) −2.25E−01 LOG STABILIZERPERCENT IC5 IN OVERHEAD ANALYZER 2.13E−01 PRESSURE COMPENSATED STABILZERTOWER TRAY 15 TEMPERATURE −1.94E−01 GASOLINE STRIPPER REBOILER OUTLETTEMPERATURE 1.78E−01 PRESSURE COMPENSATED SPLITTER TOWER FEED TRAYTEMPERATURE −1.68E−01 AMBIENT TEMPERATURE −1.67E−01 5. Change in OilConversion GASOLINE STRIPPER REBOILER OUTLET TEMPERATURE −2.93E−01SPLITTER TOWER LEVEL −2.62E−01 PRESSURE COMPENSATED STABILIZER TOWERTRAY 33 TEMPERATURE −2.46E−01 PRESSURE COMPENSATED STABILIZER TOWER TRAY25 TEMPERATURE −2.38E−01 SPLITTER TOWER BOTTOMS TEMPERATURE 2.13E−01PRESSURE COMPENSATED STABILIZER TOWER TRAY 2 TEMPERATURE 2.12E−01PRESSURE COMPENSATED STABILIZER TOWER TRAY 8 TEMPERATURE 2.12E−01GASOLINE STRIPPER LEVEL −2.11E−01 LIGHT NAPTHA DRAW TO SPLITTER FEEDFLOW RATIO 2.00E−01 STABILZER SIDE DRAW TO TOTAL BOTTOMS FLOW RATIO1.91E−01 6. Splitter Product Draws GASOLINE DRAW TO TOTAL PRODUCT FLOWRATIO −3.31E−01 PRESSURE COMPENSATED STABILIZER TOWER TRAY 8 TEMPERATURE2.78E−01 KERO/HEAVY NAPTHA DRAW TO SPLITTER FEED FLOW RATIO −2.75E−01LIGHT NAPTHA DRAW TO SPLITTER FEED FLOW RATIO −2.74E−01 PRESSURECOMPENSATED STABILIZER TOWER TRAY 2 TEMPERATURE 2.69E−01 STABILIZERTOWER OVERHEAD PRESSURE −2.50E−01 PRESSURE COMPENSATED STABILIZER TOWERTRAY 15 TEMPERATURE 2.47E−01 STABILIZER SIDE DRAW TO BOTTOMS FLOW RATIO−2.46E−01 STABILIZER TOWER INLET TEMPERATURE 2.37E−01 SPLITTER BOTTOMSTO TOTAL FEED FLOW RATIO −2.25E−01 7. Stabilizer Feed Quality STABILIZERREBOILER CALCULATED HEAT INPUT TO FEED FLOW RATIO 3.87E−01 2ND STAGE LPSBOTTOMS FLOW TO TOTAL PRODUCT FLOW RATIO −3.43E−01 STABILIZER DISTILLATERATIO (REFLUX TO OVERHEAD PROD FLOW) −2.93E−01 STABILIZER BOTTOMS FLOWTO TOTAL PRODUCT FLOW RATIO −2.93E−01 1ST STAGE LPS BOTTOMS FLOW TOTOTAL FEED FLOW RATIO −2.69E−01 BUTANE DRAW TO TOTAL PRODUCT FLOW RATIO2.62E−01 PRESSURE COMPENSATED STABILIZER TOWER TRAY 40 TEMPERATURE−2.30E−01 1ST STAGE LPS BOTTOMS TO TOTAL FEED FLOW RATIO −2.19E−01GASOLINE STRIPPER FLASH VAPOR TEMPERATURE −1.67E−01 STABILIZER TOWEROVERHEAD TEMPERATURE −1.66E−01 8. Stabilizer Butane Effects STABILIZERREBOILER CALCULATED HEAT INPUT TO FEED FLOW RATIO 5.22E−01 2ND STAGE LPSBOTTOMS TO TOTAL PRODUCT FLOW RATIO −4.70E−01 STABILIZER BOTTOMS TOTOTAL PRODUCT-FLOW RATIO 2.33E−01 LOG NC4 IN GASOLINE ANALYZER −2.17E−01GASOLINE DRAW TO TOTAL PRODUCT FLOW RATIO −1.98E−01 PRESSURE COMPENSATEDSTABILIZER TOWER TRAY 40 TEMPERATURE 1.89E−01 STABILIZER DISTILLATERATIO (REFLUX TO OVERHEAD PROD FLOW) 1.80E−01 BUTANE DRAW TO TOTALPRODUCT FLOW RATIO −1.65E−01 GASOLINE STRIPPER FLASH VAPOR TEMPERATURE1.58E−01 SPLITTER OVERHEAD ACCUMULATOR LEVEL −1.52E−01 9. StabilizerPressure Balance 2ND STAGE LPS BOTTOMS TO TOTAL PRODUCT FLOW RATIO3.18E−01 STABILIZER TOWER OVERHEAD PRESSURE 3.06E−01 LIGHT NAPTHA DRAWTO TOTAL SPLITTER FEED FLOW RATIO −2.67E−01 SPLITTER TOWER OVERHEADTEMPERATURE −2.54E−01 STABILIZER REBOILER CALCULATED HEAT INPUT TO FEEDFLOW RATIO −2.39E−01 STABILIZER SIDE DRAW TO TOTAL BOTTOMS FLOW RATIO2.19E−01 PRESSURE COMPENSATED SPLITTER TOWER TRAY 10 TEMPERATURE2.05E−01 SPLITTER REBOILER CALCULATED HEAT INPUT TO FEED FLOW RATIO1.96E−01 PRESSURE COMPENSATED SPLITTER TOWER TRAY 38 TEMPERATURE−1.89E−01 SPLITTER OVERHEAD ACCUMULATOR LEVEL −1.83E−01 10. Total EnergyInputs SPLITTER REBOILER FIRED DUTY 5.83E−01 STABILIZER REBOILER FIREDDUTY 5.47E−01 SPLITTER PUMPAROUND HEATER COOLER TO SPLITTER FEED RATIO−1.77E−01 GASOLINE STRIPPER OUTLET TEMPERATURE −1.60E−01 SPLITTERBOTTOMS TO TOTAL FEED RATIO FLOW RATIO −1.46E−01 STABILIZER TOWER INLETTEMPERATURE −1.45E−01 STABILIZER SIDE DRAW TO BOTTOMS FLOW RATIO1.40E−01 STABILIZER TOWER LEVEL 1.28E−01 SPLITTER TOWER LEVEL 1.23E−011ST STAGE LPS BOTTOMS TO TOTAL FEED FLOW RATIO −1.18E−01 11. SplitterFractionation Change SPLITTER REFLUX TO TOTAL FEED FLOW RATIO −3.47E−01SPLITTER REBOILER FIRED DUTY −3.12E−01 STABILIZER SIDE DRAW TO BOTTOMSFLOW RATIO 3.08E−01 GASOLINE DRAW TO TOTAL PRODUCT FLOW RATIO 2.81E−01PRESSURE COMPENSATED SPLITTER TOWER FEED TRAY TEMPERATURE −2.45E−01STABILIZER REBOILER FIRED DUTY −2.44E−01 GASOLINE STRIPPER OUTLETTEMPERATURE −2.26E−01 SPLITTER TOWER OVERHEAD TEMPERATURE 2.20E−01 LOGSTABILIZER PERCENT IC5 IN OVERHEAD −2.08E−01 STABILIZER TOWER LEVEL1.86E−01 12. Butane SPLITTER REFLUX TO TOTAL FEED FLOW RATIO −4.00E−01LOG NC4 IN GASOLINE ANALYZER −2.82E−01 SPLITTER TOWER LEVEL 2.51E−01SPLITTER TOWER OVERHEAD TEMPERATURE 2.48E−01 STABILIZER TOWER OVERHEADPRESSURE 2.47E−01 SPLITTER TOWER BOTTOMS TEMPERATURE −2.23E−01STABILIZER TOWER INLET TEMPERATURE 2.22E−01 GASOLINE STRIPPER OUTLETTEMPERATURE 2.07E−01 GASOLINE STRIPPER LEVEL −1.96E−01 PRESSURECOMPENSATED SPLITTER TOWER FEED TRAY TEMPERATURE 1.80E−01 13. TowerInventory SPLITTER TOWER OVERHEAD PRESSURE 4.30E−01 STABILIZER TOWEROVERHEAD PRESSURE 4.04E−01 SPLITTER TOWER LEVEL −3.38E−01 SPLITTEROVERHEAD ACCUMULATOR LEVEL −2.31E−01 GASOLINE STRIPPER LEVEL 2.30E−01SPLITTER TOWER BOTTOMS TEMPERATURE 2.06E−01 LOG NC4 IN GASOLINE ANALYZER1.94E−01 STABILIZER TOWER INLET TEMPERATURE 1.91E−01 1ST STAGE SURGEDRUM OUTLET TEMPERATURE 1.69E−01 PRESSURE COMPENSATED STABILZER TOWERTRAY 40 TEMPERATURE −1.56E−01 14. Feed Quality/Overhead DYNAMICCOMPENSATED FEED API ANALYZER 4.54E−01 HEAVY NAPHTHA 90% BP ANALYZER3.04E−01 STABILIZER TOWER OVERHEAD PRESSURE −2.79E−01 LOG NC4 INGASOLINE ANALYZER −2.52E−01 STABILIZER TOWER LEVEL −2.49E−01 SPLITTERTOWER LEVEL −2.39E−01 1ST STAGE SURGE DRUM OUTLET TEMPERATURE 1.99E−01GASOLINE STRIP FLASH VAPOR TEMERATURE −1.84E−01 SPLITTER OVERHEADACCUMULATOR LEVEL −1.77E−01 GASOLINE STRIPPER OUTLET TEMPERATURE1.72E−01 15. Feed Quality/Gasoline HEAVY NAPHTHA 90% BP ANALYZER6.15E−01 DYNAMIC COMPENSATED FEED API ANALYZER −4.00E−01 GASOLINESTRIPPER LEVEL −3.74E−01 SPLITTER REFLUX FLOW TO FEED FLOW RATIO1.86E−01 STABILIZER TOWER LEVEL −1.76E−01 SPLITTER PUMPAROUND HEATERCOOLER TO SPLITTER FEED RATIO −1.74E−01 SPLITTER TOWER OVERHEAD PRESSURE1.35E−01 HEAVY NAPTHA STRIPPER FLASH VAPOR TEMPERATURE 1.26E−01 HEAVYNAPTHA STRIPPER BOTTOMS TEMPERATURE 1.26E−01 LIGHT NAPTHA DRAW TOSPLITTER FEED FLOW RATIO −1.21E−01

TABLE 4 R1R2 Reactor Stability Monitor Amplitude Amplitude MeasurementCategory Size Increasing Range R1 Total Quench Flow R1 Temp/Total QuenchCycling X X R2 Total Quench Flow R2 Temp/Total Quench Cycling X X R1Total Differential Temperature R1 Temp/Total Quench Cycling X X R1 Bed 6Differential Temperature R1 Temp/Total Quench Cycling X X R1 Bed 6 InletTemperature R1 Temp/Total Quench Cycling X X R2 Total DifferentialTemperature R2 Temp/Total Quench Cycling X X R2 Bed 1 DifferentialTemperature R2 Temp/Total Quench Cycling X X R2 Bed 2 Inlet TemperatureR2 Temp/Total Quench Cycling X X R2 Bed 3 Inlet Temperature R2Temp/Total Quench Cycling X X R2 Bed 4 Inlet Temperature R2 Temp/TotalQuench Cycling X X R2 Bed 5 Inlet Temperature R2 Temp/Total QuenchCycling X X R1R2 Offgas Component R1R2 Offgas Measurement X X XVariability

TABLE 5 R3 Reactor Stability Monitor Amplitude Amplitude MeasurementCategory Size Increasing Range R3 Bed 2 Inlet Temperature R3 TemperatureCycling X X R3 Bed 2 Quench Flow R3 Quench Flow Cycling X X R3 Bed 3Inlet Temperature R3 Temperature Cycling X X R3 Bed 3 Quench Flow R3Quench Flow Cycling X X R3 Bed 4 Inlet Temperature R3 TemperatureCycling X X R3 Bed 4 Quench Flow R3 Quench Flow Cycling X X R3 Bed 5Inlet Temperature R3 Temperature Cycling X X R3 Bed 5 Quench Flow R3Quench Flow Cycling X X R3 Offgas Component R3 Offgas MeasurementVariability X X X

TABLE 6 Separator Level Engineering Model Characteristics Process AreaMeasurement Frozen Cycling Range Cross-Validation 1st Stg LP SeparatorPrimary Level Measurement X X X X Secondary Level Measurement X X X X1st Stg HP Separator Primary Level Measurement X X X X Secondary LevelMeasurement X X X X 2nd Stg LP Separator Primary Level Measurement X X XX Secondary Level Measurement X X X X 2nd Stg HP Separator Primary LevelMeasurement X X X Secondary Level Measurement X X X Tertiary LevelMeasurement X X X

1. A method for abnormal event detection (AED) for some of process unitsof a hydrocracker unit of a petroleum refinery comprised of: (a)comparing online measurements from the process unit to a set of modelsfor normal operation of the corresponding process units, (b) determiningif the current operation differs from expected normal operations so asto indicate the presence of an abnormal condition in a process unit, (c)assisting the process operator to determine the underlying cause of anabnormal condition in the HDC process unit, and (d) performingcorrective action to return the unit to normal operation.
 2. The methodof claim 1 wherein said set of models correspond to equipment groups andoperating modes, one model for each group which may include one or moreoperating modes.
 3. The method of claim 1 wherein said set of modelscorrespond to equipment groups and process operating modes, one modelfor each group and each mode.
 4. The method of claim 2 wherein saidequipment groups include all major material and energy interactions inthe same group.
 5. The method of claim 4 wherein said equipment groupsinclude quick recycles in the same group.
 6. The method of claim 5wherein said set of models of normal operations include principlecomponent models.
 7. The method of 6 wherein set of models of normaloperations includes engineering models.
 8. The method of claim 1 whereinsaid set of models of normal operation for each process unit is either aPrinciple Components model or an engineering model.
 9. The method ofclaim 8 wherein a hydrocracker process unit is partitioned intofunctional sections with a Principle Components model for each section.10. The method of claim 9 where there are three functional sections. 11.The method of claim 4 wherein said Principle Components include processvariables provided by online measurements.
 12. The method of claim 10wherein the three functional sections of the hydrocracking process unitinclude: 1st stage hydrotreating reactor (R1), 2nd stage hydrocrackingreactor (R2), 3rd stage hydrocracking reactor (R3), 1st & 2nd stageLP/HP separators, stabilizer tower, splitter tower, and the reciprocalcompressor.
 13. The method of claim 6 further comprising additionalmodels to determine the consistency between selected control valves andflow meters, process analyzers and secondary measurements, and the onsetof temperature and pressures oscillations in the reactor beds.
 14. Themethod of claim 4 wherein said model further comprises suppressing modelcalculates to eliminate operator induced notifications and falsepositives.
 15. The method of claim 2 wherein: (a) deriving said modelbegins with obtaining an initial model based upon questionable data, (b)use of said initial model to refine the data and improve the model, and(c) iteratively repeating step (b) to improve the model.
 16. The methodof claim 9 wherein said training data set includes historical data ofthe processing unit for model development.
 17. The method of claim 9wherein said model includes transformed variables.
 18. The method ofclaim 9 wherein said transformed variables include reflux to totalproduct flow in distillation columns, log of composition and overheadpressure in distillation columns, pressure compensated temperaturemeasurements, flow to valve position and bed differential temperatureand pressure.
 19. The model of claim 9 wherein some measurement pairsare time synchronized to one of the variables using a dynamic filter.20. The model of claim 9 wherein the process measurement variablesaffected by operating point changes in the process operations areconverted to deviation variables.
 21. The method of claim 9 wherein themeasurements of a variable are scaled prior to model identification 22.The method of claim 19 wherein the measurements are scaled by theexpected normal range of that variable.
 23. The method of claim 9wherein the number of principle components is selected by the magnitudeof total process variation represented by successive components.
 24. Asystem for abnormal event detection (AED) for some of the hydrocrackerprocess units of a petroleum refinery comprised of: (a) a set of modelsfor the process units describing operations of the process units, (b) adisplay which indicates if the current operation differs from expectednormal operations so as to indicate the presence of an abnormalcondition in the process unit, (c) a display which indicates theunderlying cause of an abnormal condition in the HDC process unit. 25.The system of claim 24 wherein said model for each process unit iseither a Principle Components model or an engineering model.
 26. Thesystem of claim 24 wherein a hydrocracker unit is partitioned into threeoperational sections with a Principle Components model for each section.27. The system of claim 26 wherein said Principle Components includeprocess variables provided by online measurements.
 28. The system ofclaim 26 wherein the three operational sections of the hydrocrackingprocess unit include: 1st stage hydrotreating reactor (R1), 2nd stagehydrocracking reactor (R2), 3rd stage hydrocracking reactor (R3), 1stand 2nd stage LP/HP separators, stabilizer tower, splitter tower, andthe reciprocal compressor.
 29. The system of claim 28 wherein additionalmodels determine the consistency between selected control valves andflow meters, process analyzers and secondary measurements, and the onsetof temperature and pressures oscillations in the reactor beds.
 30. Thesystem of claim 26 wherein said model further comprises suppressingmodel calculates to eliminate operator induced notifications and falsepositives.
 31. The system of claim 25 wherein: (a) deriving said modelbegins with obtaining an initial model based upon questionable data, (b)use of said initial model to refine the data and improve the model, and(c) iteratively repeating step (b) to improve the model.
 32. The systemof claim 31 wherein said training data set includes historical data ofthe processing unit for model development.
 33. The system of claim 32wherein said model includes transformed variables.
 34. The system ofclaim 33 wherein said transformed variables include reflux to totalproduct flow in distillation columns, log of composition and overheadpressure in distillation columns, pressure compensated temperaturemeasurements, flow to valve position and bed differential temperatureand pressure.
 35. The system of claim 32 wherein some measurement pairsare time synchronized to one of the variables using a dynamic filter.36. The system of claim 32 wherein the process measurement variablesaffected by operating point changes in the process operations areconverted to deivation variables.
 37. The system of claim 32 wherein themeasurements of a variable are scaled prior to model identification. 38.The system of claim 37 wherein the measurements are scaled by theexpected normal range of that variable.
 39. The system of claim 32wherein the number of principle components is selected by the magnitudeof total process variation represented by successive components.